Release candidate 0.2.17rc1

.github/TRIAGERS.md

@@ -1,2 +1,2 @@
 # This file documents Triage members in the Llama Stack community
- @franciscojavierarceo
+ @bbrowning @franciscojavierarceo @leseb

.github/actions/run-and-record-tests/action.yml

@@ -2,13 +2,9 @@ name: 'Run and Record Tests'
 description: 'Run integration tests and handle recording/artifact upload'
 
 inputs:
-  test-subdirs:
+  test-types:
-    description: 'Comma-separated list of test subdirectories to run'
+    description: 'JSON array of test types to run'
     required: true
-  test-pattern:
-    description: 'Regex pattern to pass to pytest -k'
-    required: false
-    default: ''
   stack-config:
     description: 'Stack configuration to use'
     required: true
@@ -36,14 +32,12 @@ runs:
     - name: Run Integration Tests
       shell: bash
       run: |
-        uv run --no-sync ./scripts/integration-tests.sh \
+        ./scripts/integration-tests.sh \
           --stack-config '${{ inputs.stack-config }}' \
           --provider '${{ inputs.provider }}' \
-          --test-subdirs '${{ inputs.test-subdirs }}' \
+          --test-types '${{ inputs.test-types }}' \
-          --test-pattern '${{ inputs.test-pattern }}' \
           --inference-mode '${{ inputs.inference-mode }}' \
-          ${{ inputs.run-vision-tests == 'true' && '--run-vision-tests' || '' }} \
+          ${{ inputs.run-vision-tests == 'true' && '--run-vision-tests' || '' }}
-          | tee pytest-${{ inputs.inference-mode }}.log
 
 
     - name: Commit and push recordings
@@ -63,10 +57,10 @@ runs:
             git commit -m "Recordings update from CI"
           fi
 
-          git fetch origin ${{ github.ref_name }}
+          git fetch origin ${{ github.event.pull_request.head.ref }}
-          git rebase origin/${{ github.ref_name }}
+          git rebase origin/${{ github.event.pull_request.head.ref }}
           echo "Rebased successfully"
-          git push origin HEAD:${{ github.ref_name }}
+          git push origin HEAD:${{ github.event.pull_request.head.ref }}
           echo "Pushed successfully"
         else
           echo "No recording changes"

.github/actions/setup-runner/action.yml

@@ -16,21 +16,19 @@ runs:
       uses: astral-sh/setup-uv@6b9c6063abd6010835644d4c2e1bef4cf5cd0fca # v6.0.1
       with:
         python-version: ${{ inputs.python-version }}
+        activate-environment: true
         version: 0.7.6
 
     - name: Install dependencies
       shell: bash
       run: |
-        echo "Updating project dependencies via uv sync"
         uv sync --all-groups
-
+        uv pip install ollama faiss-cpu
-        echo "Installing ad-hoc dependencies"
-        uv pip install faiss-cpu
 
         # Install llama-stack-client-python based on the client-version input
         if [ "${{ inputs.client-version }}" = "latest" ]; then
           echo "Installing latest llama-stack-client-python from main branch"
-          uv pip install git+https://github.com/llamastack/llama-stack-client-python.git@main
+          uv pip install git+https://github.com/meta-llama/llama-stack-client-python.git@main
         elif [ "${{ inputs.client-version }}" = "published" ]; then
           echo "Installing published llama-stack-client-python from PyPI"
           uv pip install llama-stack-client
@@ -39,5 +37,4 @@ runs:
           exit 1
         fi
 
-        echo "Installed llama packages"
+        uv pip install -e .
-        uv pip list | grep llama

.github/actions/setup-test-environment/action.yml

@@ -42,22 +42,7 @@ runs:
     - name: Build Llama Stack
       shell: bash
       run: |
-        # Install llama-stack-client-python based on the client-version input
+        uv run llama stack build --template ci-tests --image-type venv
-        if [ "${{ inputs.client-version }}" = "latest" ]; then
-          echo "Installing latest llama-stack-client-python from main branch"
-          export LLAMA_STACK_CLIENT_DIR=git+https://github.com/llamastack/llama-stack-client-python.git@main
-        elif [ "${{ inputs.client-version }}" = "published" ]; then
-          echo "Installing published llama-stack-client-python from PyPI"
-          unset LLAMA_STACK_CLIENT_DIR
-        else
-          echo "Invalid client-version: ${{ inputs.client-version }}"
-          exit 1
-        fi
-
-        echo "Building Llama Stack"
-
-        LLAMA_STACK_DIR=. \
-          uv run --no-sync llama stack build --template ci-tests --image-type venv
 
     - name: Configure git for commits
       shell: bash

.github/dependabot.yml

@@ -9,7 +9,6 @@ updates:
       day: "saturday"
     commit-message:
       prefix: chore(github-deps)
-
   - package-ecosystem: "uv"
     directory: "/"
     schedule:
@@ -20,14 +19,3 @@ updates:
       - python
     commit-message:
       prefix: chore(python-deps)
-
-  - package-ecosystem: npm
-    directory: "/llama_stack/ui"
-    schedule:
-      interval: "weekly"
-      day: "saturday"
-    labels:
-      - type/dependencies
-      - javascript
-    commit-message:
-      prefix: chore(ui-deps)

.github/workflows/README.md

@@ -18,6 +18,5 @@ Llama Stack uses GitHub Actions for Continuous Integration (CI). Below is a tabl
 | Close stale issues and PRs | [stale_bot.yml](stale_bot.yml) | Run the Stale Bot action |
 | Test External Providers Installed via Module | [test-external-provider-module.yml](test-external-provider-module.yml) | Test External Provider installation via Python module |
 | Test External API and Providers | [test-external.yml](test-external.yml) | Test the External API and Provider mechanisms |
-| UI Tests | [ui-unit-tests.yml](ui-unit-tests.yml) | Run the UI test suite |
 | Unit Tests | [unit-tests.yml](unit-tests.yml) | Run the unit test suite |
 | Update ReadTheDocs | [update-readthedocs.yml](update-readthedocs.yml) | Update the Llama Stack ReadTheDocs site |

.github/workflows/changelog.yml

@@ -17,7 +17,7 @@ jobs:
       pull-requests: write  # for peter-evans/create-pull-request to create a PR
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
         with:
           ref: main
           fetch-depth: 0

.github/workflows/install-script-ci.yml

@@ -16,22 +16,21 @@ jobs:
   lint:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # 5.0.0
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2
       - name: Run ShellCheck on install.sh
         run: shellcheck scripts/install.sh
   smoke-test-on-dev:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
 
       - name: Build a single provider
         run: |
-          USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run --no-sync \
+          USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --template starter --image-type container --image-name test
-            llama stack build --template starter --image-type container --image-name test
 
       - name: Run installer end-to-end
         run: |

.github/workflows/integration-auth-tests.yml

@@ -10,7 +10,6 @@ on:
     paths:
       - 'distributions/**'
       - 'llama_stack/**'
-      - '!llama_stack/ui/**'
       - 'tests/integration/**'
       - 'uv.lock'
       - 'pyproject.toml'
@@ -31,7 +30,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/integration-sql-store-tests.yml

@@ -44,7 +44,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/integration-tests.yml

@@ -10,7 +10,6 @@ on:
     types: [opened, synchronize, reopened]
     paths:
       - 'llama_stack/**'
-      - '!llama_stack/ui/**'
       - 'tests/**'
       - 'uv.lock'
       - 'pyproject.toml'
@@ -32,14 +31,6 @@ on:
         description: 'Test against a specific provider'
         type: string
         default: 'ollama'
-      test-subdirs:
-        description: 'Comma-separated list of test subdirectories to run'
-        type: string
-        default: ''
-      test-pattern:
-        description: 'Regex pattern to pass to pytest -k'
-        type: string
-        default: ''
 
 concurrency:
   # Skip concurrency for pushes to main - each commit should be tested independently
@@ -47,8 +38,27 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
+  discover-tests:
+    runs-on: ubuntu-latest
+    outputs:
+      test-types: ${{ steps.generate-test-types.outputs.test-types }}
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: Generate test types
+        id: generate-test-types
+        run: |
+          # Get test directories dynamically, excluding non-test directories
+          # NOTE: we are excluding post_training since the tests take too long
+          TEST_TYPES=$(find tests/integration -maxdepth 1 -mindepth 1 -type d -printf "%f\n" |
+            grep -Ev "^(__pycache__|fixtures|test_cases|recordings|post_training)$" |
+            sort | jq -R -s -c 'split("\n")[:-1]')
+          echo "test-types=$TEST_TYPES" >> $GITHUB_OUTPUT
 
   run-replay-mode-tests:
+    needs: discover-tests
     runs-on: ubuntu-latest
     name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, vision={4})', matrix.client-type, matrix.provider, matrix.python-version, matrix.client-version, matrix.run-vision-tests) }}
 
@@ -65,7 +75,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Setup test environment
         uses: ./.github/actions/setup-test-environment
@@ -79,8 +89,7 @@ jobs:
       - name: Run tests
         uses: ./.github/actions/run-and-record-tests
         with:
-          test-subdirs: ${{ inputs.test-subdirs }}
+          test-types: ${{ needs.discover-tests.outputs.test-types }}
-          test-pattern: ${{ inputs.test-pattern }}
           stack-config: ${{ matrix.client-type == 'library' && 'ci-tests' || 'server:ci-tests' }}
           provider: ${{ matrix.provider }}
           inference-mode: 'replay'

.github/workflows/integration-vector-io-tests.yml

@@ -9,17 +9,14 @@ on:
     branches: [ main ]
     paths:
       - 'llama_stack/**'
-      - '!llama_stack/ui/**'
       - 'tests/integration/vector_io/**'
       - 'uv.lock'
       - 'pyproject.toml'
       - 'requirements.txt'
       - '.github/workflows/integration-vector-io-tests.yml' # This workflow
-  schedule:
-    - cron: '0 0 * * *'  # (test on python 3.13) Daily at 12 AM UTC
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
+  group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
 jobs:
@@ -28,12 +25,12 @@ jobs:
     strategy:
       matrix:
         vector-io-provider: ["inline::faiss", "inline::sqlite-vec", "inline::milvus", "remote::chromadb", "remote::pgvector", "remote::weaviate", "remote::qdrant"]
-        python-version: ${{ github.event.schedule == '0 0 * * *' && fromJSON('["3.12", "3.13"]') || fromJSON('["3.12"]') }}
+        python-version: ["3.12", "3.13"]
       fail-fast: false # we want to run all tests regardless of failure
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
@@ -144,7 +141,7 @@ jobs:
 
       - name: Build Llama Stack
         run: |
-          uv run --no-sync llama stack build --template ci-tests --image-type venv
+          uv run llama stack build --template ci-tests --image-type venv
 
       - name: Check Storage and Memory Available Before Tests
         if: ${{ always() }}
@@ -167,10 +164,9 @@ jobs:
           ENABLE_WEAVIATE: ${{ matrix.vector-io-provider == 'remote::weaviate' && 'true' || '' }}
           WEAVIATE_CLUSTER_URL: ${{ matrix.vector-io-provider == 'remote::weaviate' && 'localhost:8080' || '' }}
         run: |
-          uv run --no-sync \
+          uv run pytest -sv --stack-config="inference=inline::sentence-transformers,vector_io=${{ matrix.vector-io-provider }}" \
-            pytest -sv --stack-config="files=inline::localfs,inference=inline::sentence-transformers,vector_io=${{ matrix.vector-io-provider }}" \
             tests/integration/vector_io \
-            --embedding-model inline::sentence-transformers/all-MiniLM-L6-v2
+            --embedding-model sentence-transformers/all-MiniLM-L6-v2
 
       - name: Check Storage and Memory Available After Tests
         if: ${{ always() }}

.github/workflows/pre-commit.yml

@@ -20,7 +20,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
         with:
           # For dependabot PRs, we need to checkout with a token that can push changes
           token: ${{ github.actor == 'dependabot[bot]' && secrets.GITHUB_TOKEN || github.token }}
@@ -36,21 +36,6 @@ jobs:
             **/requirements*.txt
             .pre-commit-config.yaml
 
-      # npm ci may fail -
-      #   npm error `npm ci` can only install packages when your package.json and package-lock.json or npm-shrinkwrap.json are in sync. Please update your lock file with `npm install` before continuing.
-      #   npm error Invalid: lock file's llama-stack-client@0.2.17 does not satisfy llama-stack-client@0.2.18
-
-      # - name: Set up Node.js
-      #   uses: actions/setup-node@39370e3970a6d050c480ffad4ff0ed4d3fdee5af # v4.1.0
-      #   with:
-      #     node-version: '20'
-      #     cache: 'npm'
-      #     cache-dependency-path: 'llama_stack/ui/'
-
-      # - name: Install npm dependencies
-      #   run: npm ci
-      #   working-directory: llama_stack/ui
-
       - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
         continue-on-error: true
         env:

.github/workflows/providers-build.yml

@@ -36,7 +36,7 @@ jobs:
       distros: ${{ steps.set-matrix.outputs.distros }}
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Generate Distribution List
         id: set-matrix
@@ -55,7 +55,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
@@ -79,7 +79,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
@@ -92,7 +92,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
@@ -117,7 +117,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/python-build-test.yml

@@ -9,8 +9,6 @@ on:
   pull_request:
     branches:
       - main
-    paths-ignore:
-        - 'llama_stack/ui/**'
 
 jobs:
   build:
@@ -21,10 +19,10 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+      uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
     - name: Install uv
-      uses: astral-sh/setup-uv@d9e0f98d3fc6adb07d1e3d37f3043649ddad06a1 # v6.5.0
+      uses: astral-sh/setup-uv@e92bafb6253dcd438e0484186d7669ea7a8ca1cc # v6.4.3
       with:
         python-version: ${{ matrix.python-version }}
         activate-environment: true

.github/workflows/record-integration-tests.yml

@@ -1,53 +1,93 @@
-# This workflow should be run manually when needing to re-record tests. This happens when you have
-#  - added a new test
-#  - or changed an existing test such that a new inference call is made
-# You should make a PR and then run this workflow on that PR branch. The workflow will re-record the
-# tests and commit the recordings to the PR branch.
 name: Integration Tests (Record)
 
 run-name: Run the integration test suite from tests/integration
 
 on:
+  pull_request:
+    branches: [ main ]
+    types: [opened, synchronize, labeled]
+    paths:
+      - 'llama_stack/**'
+      - 'tests/**'
+      - 'uv.lock'
+      - 'pyproject.toml'
+      - '.github/workflows/record-integration-tests.yml' # This workflow
+      - '.github/actions/setup-ollama/action.yml'
+      - '.github/actions/setup-test-environment/action.yml'
+      - '.github/actions/run-and-record-tests/action.yml'
   workflow_dispatch:
     inputs:
-      test-subdirs:
-        description: 'Comma-separated list of test subdirectories to run'
-        type: string
-        default: ''
       test-provider:
         description: 'Test against a specific provider'
         type: string
         default: 'ollama'
-      run-vision-tests:
+
-        description: 'Whether to run vision tests'
+concurrency:
-        type: boolean
+  group: ${{ github.workflow }}-${{ github.ref }}
-        default: false
+  cancel-in-progress: true
-      test-pattern:
-        description: 'Regex pattern to pass to pytest -k'
-        type: string
-        default: ''
 
 jobs:
+  discover-tests:
+    if: contains(github.event.pull_request.labels.*.name, 're-record-tests') ||
+      contains(github.event.pull_request.labels.*.name, 're-record-vision-tests')
+    runs-on: ubuntu-latest
+    outputs:
+      test-types: ${{ steps.generate-test-types.outputs.test-types }}
+      matrix-modes: ${{ steps.generate-test-types.outputs.matrix-modes }}
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: Generate test types
+        id: generate-test-types
+        run: |
+          # Get test directories dynamically, excluding non-test directories
+          TEST_TYPES=$(find tests/integration -maxdepth 1 -mindepth 1 -type d -printf "%f\n" |
+            grep -Ev "^(__pycache__|fixtures|test_cases|recordings|post_training)$" |
+            sort | jq -R -s -c 'split("\n")[:-1]')
+          echo "test-types=$TEST_TYPES" >> $GITHUB_OUTPUT
+
+          labels=$(gh pr view ${{ github.event.pull_request.number }} --json labels --jq '.labels[].name')
+          echo "labels=$labels"
+
+          modes_array=()
+          if [[ $labels == *"re-record-vision-tests"* ]]; then
+            modes_array+=("vision")
+          fi
+          if [[ $labels == *"re-record-tests"* ]]; then
+            modes_array+=("non-vision")
+          fi
+
+          # Convert to JSON array
+          if [ ${#modes_array[@]} -eq 0 ]; then
+            matrix_modes="[]"
+          else
+            matrix_modes=$(printf '%s\n' "${modes_array[@]}" | jq -R -s -c 'split("\n")[:-1]')
+          fi
+          echo "matrix_modes=$matrix_modes"
+          echo "matrix-modes=$matrix_modes" >> $GITHUB_OUTPUT
+
+        env:
+          GH_TOKEN: ${{ github.token }}
+
   record-tests:
+    needs: discover-tests
     runs-on: ubuntu-latest
 
     permissions:
       contents: write
 
-    steps:
+    strategy:
-      - name: Echo workflow inputs
+      fail-fast: false
-        run: |
+      matrix:
-          echo "::group::Workflow Inputs"
+        mode: ${{ fromJSON(needs.discover-tests.outputs.matrix-modes) }}
-          echo "test-subdirs: ${{ inputs.test-subdirs }}"
-          echo "test-provider: ${{ inputs.test-provider }}"
-          echo "run-vision-tests: ${{ inputs.run-vision-tests }}"
-          echo "test-pattern: ${{ inputs.test-pattern }}"
-          echo "branch: ${{ github.ref_name }}"
-          echo "::endgroup::"
 
+    steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
         with:
+          ref: ${{ github.event.pull_request.head.ref }}
           fetch-depth: 0
 
       - name: Setup test environment
@@ -56,15 +96,14 @@ jobs:
           python-version: "3.12"  # Use single Python version for recording
           client-version: "latest"
           provider: ${{ inputs.test-provider || 'ollama' }}
-          run-vision-tests: ${{ inputs.run-vision-tests }}
+          run-vision-tests: ${{ matrix.mode == 'vision' && 'true' || 'false' }}
           inference-mode: 'record'
 
       - name: Run and record tests
         uses: ./.github/actions/run-and-record-tests
         with:
-          test-pattern: ${{ inputs.test-pattern }}
+          test-types: ${{ needs.discover-tests.outputs.test-types }}
-          test-subdirs: ${{ inputs.test-subdirs }}
           stack-config: 'server:ci-tests'  # recording must be done with server since more tests are run
           provider: ${{ inputs.test-provider || 'ollama' }}
           inference-mode: 'record'
-          run-vision-tests: ${{ inputs.run-vision-tests }}
+          run-vision-tests: ${{ matrix.mode == 'vision' && 'true' || 'false' }}

.github/workflows/semantic-pr.yml

@@ -11,7 +11,7 @@ on:
       - synchronize
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number }}
+  group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
 permissions:
@@ -22,6 +22,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check PR Title's semantic conformance
-        uses: amannn/action-semantic-pull-request@7f33ba792281b034f64e96f4c0b5496782dd3b37 # v6.1.0
+        uses: amannn/action-semantic-pull-request@0723387faaf9b38adef4775cd42cfd5155ed6017 # v5.5.3
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/test-external-provider-module.yml

@@ -27,7 +27,7 @@ jobs:
         # container and point 'uv pip install' to the correct path...
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/test-external.yml

@@ -9,7 +9,6 @@ on:
     branches: [ main ]
     paths:
       - 'llama_stack/**'
-      - '!llama_stack/ui/**'
       - 'tests/integration/**'
       - 'uv.lock'
       - 'pyproject.toml'
@@ -27,7 +26,7 @@ jobs:
         # container and point 'uv pip install' to the correct path...
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner
@@ -44,11 +43,11 @@ jobs:
 
       - name: Print distro dependencies
         run: |
-          USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run --no-sync llama stack build --config tests/external/build.yaml --print-deps-only
+          USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --config tests/external/build.yaml --print-deps-only
 
       - name: Build distro from config file
         run: |
-          USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run --no-sync llama stack build --config tests/external/build.yaml
+          USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --config tests/external/build.yaml
 
       - name: Start Llama Stack server in background
         if: ${{ matrix.image-type }} == 'venv'

.github/workflows/ui-unit-tests.yml

@@ -1,55 +0,0 @@
-name: UI Tests
-
-run-name: Run the UI test suite
-
-on:
-  push:
-    branches: [ main ]
-  pull_request:
-    branches: [ main ]
-    paths:
-      - 'llama_stack/ui/**'
-      - '.github/workflows/ui-unit-tests.yml' # This workflow
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-jobs:
-  ui-tests:
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        node-version: [22]
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
-
-      - name: Setup Node.js
-        uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0
-        with:
-          node-version: ${{ matrix.node-version }}
-          cache: 'npm'
-          cache-dependency-path: 'llama_stack/ui/package-lock.json'
-
-      - name: Install dependencies
-        working-directory: llama_stack/ui
-        run: npm ci
-
-      - name: Run linting
-        working-directory: llama_stack/ui
-        run: npm run lint
-
-      - name: Run format check
-        working-directory: llama_stack/ui
-        run: npm run format:check
-
-      - name: Run unit tests
-        working-directory: llama_stack/ui
-        env:
-          CI: true
-
-        run: npm test -- --coverage --watchAll=false --passWithNoTests

.github/workflows/unit-tests.yml

@@ -9,7 +9,6 @@ on:
     branches: [ main ]
     paths:
       - 'llama_stack/**'
-      - '!llama_stack/ui/**'
       - 'tests/unit/**'
       - 'uv.lock'
       - 'pyproject.toml'
@@ -32,7 +31,7 @@ jobs:
           - "3.13"
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.github/workflows/update-readthedocs.yml

@@ -37,7 +37,7 @@ jobs:
       TOKEN: ${{ secrets.READTHEDOCS_TOKEN }}
     steps:
       - name: Checkout repository
-        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Install dependencies
         uses: ./.github/actions/setup-runner

.pre-commit-config.yaml

@@ -2,7 +2,6 @@ exclude: 'build/'
 
 default_language_version:
     python: python3.12
-    node: "22"
 
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
@@ -146,50 +145,6 @@ repos:
         pass_filenames: false
         require_serial: true
         files: ^.github/workflows/.*$
-      # ui-prettier and ui-eslint are disabled until we can avoid `npm ci`, which is slow and may fail -
-      #   npm error `npm ci` can only install packages when your package.json and package-lock.json or npm-shrinkwrap.json are in sync. Please update your lock file with `npm install` before continuing.
-      #   npm error Invalid: lock file's llama-stack-client@0.2.17 does not satisfy llama-stack-client@0.2.18
-      # and until we have infra for installing prettier and next via npm -
-      #   Lint UI code with ESLint.....................................................Failed
-      #   - hook id: ui-eslint
-      #   - exit code: 127
-      #   > ui@0.1.0 lint
-      #   > next lint --fix --quiet
-      #   sh: line 1: next: command not found
-      #
-      # - id: ui-prettier
-      #   name: Format UI code with Prettier
-      #   entry: bash -c 'cd llama_stack/ui && npm ci && npm run format'
-      #   language: system
-      #   files: ^llama_stack/ui/.*\.(ts|tsx)$
-      #   pass_filenames: false
-      #   require_serial: true
-      # - id: ui-eslint
-      #   name: Lint UI code with ESLint
-      #   entry: bash -c 'cd llama_stack/ui && npm run lint -- --fix --quiet'
-      #   language: system
-      #   files: ^llama_stack/ui/.*\.(ts|tsx)$
-      #   pass_filenames: false
-      #   require_serial: true
-
-      - id: check-log-usage
-        name: Ensure 'llama_stack.log' usage for logging
-        entry: bash
-        language: system
-        types: [python]
-        pass_filenames: true
-        args:
-          - -c
-          - |
-            matches=$(grep -EnH '^[^#]*\b(import\s+logging|from\s+logging\b)' "$@" | grep -v -e '#\s*allow-direct-logging' || true)
-            if [ -n "$matches" ]; then
-              # GitHub Actions annotation format
-              while IFS=: read -r file line_num rest; do
-                echo "::error file=$file,line=$line_num::Do not use 'import logging' or 'from logging import' in $file. Use the custom log instead: from llama_stack.log import get_logger; logger = get_logger(). If direct logging is truly needed, add: # allow-direct-logging"
-              done <<< "$matches"
-              exit 1
-            fi
-            exit 0
 
 ci:
     autofix_commit_msg: 🎨 [pre-commit.ci] Auto format from pre-commit.com hooks

CONTRIBUTING.md

@@ -1,82 +1,13 @@
-# Contributing to Llama Stack
+# Contributing to Llama-Stack
 We want to make contributing to this project as easy and transparent as
 possible.
 
-## Set up your development environment
-
-We use [uv](https://github.com/astral-sh/uv) to manage python dependencies and virtual environments.
-You can install `uv` by following this [guide](https://docs.astral.sh/uv/getting-started/installation/).
-
-You can install the dependencies by running:
-
-```bash
-cd llama-stack
-uv sync --group dev
-uv pip install -e .
-source .venv/bin/activate
-```
-
-```{note}
-You can use a specific version of Python with `uv` by adding the `--python <version>` flag (e.g. `--python 3.12`).
-Otherwise, `uv` will automatically select a Python version according to the `requires-python` section of the `pyproject.toml`.
-For more info, see the [uv docs around Python versions](https://docs.astral.sh/uv/concepts/python-versions/).
-```
-
-Note that you can create a dotenv file `.env` that includes necessary environment variables:
-```
-LLAMA_STACK_BASE_URL=http://localhost:8321
-LLAMA_STACK_CLIENT_LOG=debug
-LLAMA_STACK_PORT=8321
-LLAMA_STACK_CONFIG=<provider-name>
-TAVILY_SEARCH_API_KEY=
-BRAVE_SEARCH_API_KEY=
-```
-
-And then use this dotenv file when running client SDK tests via the following:
-```bash
-uv run --env-file .env -- pytest -v tests/integration/inference/test_text_inference.py --text-model=meta-llama/Llama-3.1-8B-Instruct
-```
-
-### Pre-commit Hooks
-
-We use [pre-commit](https://pre-commit.com/) to run linting and formatting checks on your code. You can install the pre-commit hooks by running:
-
-```bash
-uv run pre-commit install
-```
-
-After that, pre-commit hooks will run automatically before each commit.
-
-Alternatively, if you don't want to install the pre-commit hooks, you can run the checks manually by running:
-
-```bash
-uv run pre-commit run --all-files
-```
-
-```{caution}
-Before pushing your changes, make sure that the pre-commit hooks have passed successfully.
-```
-
 ## Discussions -> Issues -> Pull Requests
 
 We actively welcome your pull requests. However, please read the following. This is heavily inspired by [Ghostty](https://github.com/ghostty-org/ghostty/blob/main/CONTRIBUTING.md).
 
 If in doubt, please open a [discussion](https://github.com/meta-llama/llama-stack/discussions); we can always convert that to an issue later.
 
-### Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
-
-Meta has a [bounty program](http://facebook.com/whitehat/info) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-### Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Meta's open source projects.
-
-Complete your CLA here: <https://code.facebook.com/cla>
-
 **I'd like to contribute!**
 
 If you are new to the project, start by looking at the issues tagged with "good first issue". If you're interested
@@ -120,15 +51,93 @@ Please avoid picking up too many issues at once. This helps you stay focused and
 
 Please keep pull requests (PRs) small and focused. If you have a large set of changes, consider splitting them into logically grouped, smaller PRs to facilitate review and testing.
 
-```{tip}
+> [!TIP]
-As a general guideline:
+> As a general guideline:
-- Experienced contributors should try to keep no more than 5 open PRs at a time.
+> - Experienced contributors should try to keep no more than 5 open PRs at a time.
-- New contributors are encouraged to have only one open PR at a time until they’re familiar with the codebase and process.
+> - New contributors are encouraged to have only one open PR at a time until they’re familiar with the codebase and process.
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Meta's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Meta has a [bounty program](http://facebook.com/whitehat/info) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+
+## Set up your development environment
+
+We use [uv](https://github.com/astral-sh/uv) to manage python dependencies and virtual environments.
+You can install `uv` by following this [guide](https://docs.astral.sh/uv/getting-started/installation/).
+
+You can install the dependencies by running:
+
+```bash
+cd llama-stack
+uv sync --group dev
+uv pip install -e .
+source .venv/bin/activate
 ```
 
-## Repository guidelines
+> [!NOTE]
+> You can use a specific version of Python with `uv` by adding the `--python <version>` flag (e.g. `--python 3.12`)
+> Otherwise, `uv` will automatically select a Python version according to the `requires-python` section of the `pyproject.toml`.
+> For more info, see the [uv docs around Python versions](https://docs.astral.sh/uv/concepts/python-versions/).
 
-### Coding Style
+Note that you can create a dotenv file `.env` that includes necessary environment variables:
+```
+LLAMA_STACK_BASE_URL=http://localhost:8321
+LLAMA_STACK_CLIENT_LOG=debug
+LLAMA_STACK_PORT=8321
+LLAMA_STACK_CONFIG=<provider-name>
+TAVILY_SEARCH_API_KEY=
+BRAVE_SEARCH_API_KEY=
+```
+
+And then use this dotenv file when running client SDK tests via the following:
+```bash
+uv run --env-file .env -- pytest -v tests/integration/inference/test_text_inference.py --text-model=meta-llama/Llama-3.1-8B-Instruct
+```
+
+## Pre-commit Hooks
+
+We use [pre-commit](https://pre-commit.com/) to run linting and formatting checks on your code. You can install the pre-commit hooks by running:
+
+```bash
+uv run pre-commit install
+```
+
+After that, pre-commit hooks will run automatically before each commit.
+
+Alternatively, if you don't want to install the pre-commit hooks, you can run the checks manually by running:
+
+```bash
+uv run pre-commit run --all-files
+```
+
+> [!CAUTION]
+> Before pushing your changes, make sure that the pre-commit hooks have passed successfully.
+
+## Running tests
+
+You can find the Llama Stack testing documentation [here](https://github.com/meta-llama/llama-stack/blob/main/tests/README.md).
+
+## Adding a new dependency to the project
+
+To add a new dependency to the project, you can use the `uv` command. For example, to add `foo` to the project, you can run:
+
+```bash
+uv add foo
+uv sync
+```
+
+## Coding Style
 
 * Comments should provide meaningful insights into the code. Avoid filler comments that simply
   describe the next step, as they create unnecessary clutter, same goes for docstrings.
@@ -148,11 +157,6 @@ As a general guideline:
   that describes the configuration. These descriptions will be used to generate the provider
   documentation.
 * When possible, use keyword arguments only when calling functions.
-* Llama Stack utilizes [custom Exception classes](llama_stack/apis/common/errors.py) for certain Resources that should be used where applicable.
-
-### License
-By contributing to Llama, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
 
 ## Common Tasks
 
@@ -206,3 +210,7 @@ uv run ./docs/openapi_generator/run_openapi_generator.sh
 ```
 
 The generated API documentation will be available in `docs/_static/`. Make sure to review the changes before committing.
+
+## License
+By contributing to Llama, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

README.md

@@ -9,7 +9,6 @@
 
 [**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb) | [**Discord**](https://discord.gg/llama-stack)
 
-
 ### ✨🎉 Llama 4 Support  🎉✨
 We released [Version 0.2.0](https://github.com/meta-llama/llama-stack/releases/tag/v0.2.0) with support for the Llama 4 herd of models released by Meta.
 
@@ -180,17 +179,3 @@ Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest
 Check out our client SDKs for connecting to a Llama Stack server in your preferred language, you can choose from [python](https://github.com/meta-llama/llama-stack-client-python), [typescript](https://github.com/meta-llama/llama-stack-client-typescript), [swift](https://github.com/meta-llama/llama-stack-client-swift), and [kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) programming languages to quickly build your applications.
 
 You can find more example scripts with client SDKs to talk with the Llama Stack server in our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repo.
-
-
-## 🌟 GitHub Star History
-## Star History
-
-[![Star History Chart](https://api.star-history.com/svg?repos=meta-llama/llama-stack&type=Date)](https://www.star-history.com/#meta-llama/llama-stack&Date)
-
-## ✨ Contributors
-
-Thanks to all of our amazing contributors!
-
-<a href="https://github.com/meta-llama/llama-stack/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=meta-llama/llama-stack" />
-</a>

docs/_static/js/keyboard_shortcuts.js

@@ -1,14 +0,0 @@
-document.addEventListener('keydown', function(event) {
-  // command+K or ctrl+K
-  if ((event.metaKey || event.ctrlKey) && event.key === 'k') {
-    event.preventDefault();
-    document.querySelector('.search-input, .search-field, input[name="q"]').focus();
-  }
-
-  // forward slash
-  if (event.key === '/' &&
-      !event.target.matches('input, textarea, select')) {
-    event.preventDefault();
-    document.querySelector('.search-input, .search-field, input[name="q"]').focus();
-  }
-});

docs/_static/llama-stack-spec.html

@@ -1452,40 +1452,6 @@
                         }
                     }
                 ]
-            },
-            "delete": {
-                "responses": {
-                    "200": {
-                        "description": "OK"
-                    },
-                    "400": {
-                        "$ref": "#/components/responses/BadRequest400"
-                    },
-                    "429": {
-                        "$ref": "#/components/responses/TooManyRequests429"
-                    },
-                    "500": {
-                        "$ref": "#/components/responses/InternalServerError500"
-                    },
-                    "default": {
-                        "$ref": "#/components/responses/DefaultError"
-                    }
-                },
-                "tags": [
-                    "Shields"
-                ],
-                "description": "Unregister a shield.",
-                "parameters": [
-                    {
-                        "name": "identifier",
-                        "in": "path",
-                        "description": "The identifier of the shield to unregister.",
-                        "required": true,
-                        "schema": {
-                            "type": "string"
-                        }
-                    }
-                ]
             }
         },
         "/v1/telemetry/traces/{trace_id}/spans/{span_id}": {
@@ -4734,49 +4700,6 @@
                 }
             }
         },
-        "/v1/openai/v1/moderations": {
-            "post": {
-                "responses": {
-                    "200": {
-                        "description": "A moderation object.",
-                        "content": {
-                            "application/json": {
-                                "schema": {
-                                    "$ref": "#/components/schemas/ModerationObject"
-                                }
-                            }
-                        }
-                    },
-                    "400": {
-                        "$ref": "#/components/responses/BadRequest400"
-                    },
-                    "429": {
-                        "$ref": "#/components/responses/TooManyRequests429"
-                    },
-                    "500": {
-                        "$ref": "#/components/responses/InternalServerError500"
-                    },
-                    "default": {
-                        "$ref": "#/components/responses/DefaultError"
-                    }
-                },
-                "tags": [
-                    "Safety"
-                ],
-                "description": "Classifies if text and/or image inputs are potentially harmful.",
-                "parameters": [],
-                "requestBody": {
-                    "content": {
-                        "application/json": {
-                            "schema": {
-                                "$ref": "#/components/schemas/RunModerationRequest"
-                            }
-                        }
-                    },
-                    "required": true
-                }
-            }
-        },
         "/v1/safety/run-shield": {
             "post": {
                 "responses": {
@@ -8293,60 +8216,28 @@
                         "type": "array",
                         "items": {
                             "type": "object",
-                            "properties": {
+                            "additionalProperties": {
-                                "attributes": {
+                                "oneOf": [
-                                    "type": "object",
+                                    {
-                                    "additionalProperties": {
+                                        "type": "null"
-                                        "oneOf": [
-                                            {
-                                                "type": "null"
-                                            },
-                                            {
-                                                "type": "boolean"
-                                            },
-                                            {
-                                                "type": "number"
-                                            },
-                                            {
-                                                "type": "string"
-                                            },
-                                            {
-                                                "type": "array"
-                                            },
-                                            {
-                                                "type": "object"
-                                            }
-                                        ]
                                     },
-                                    "description": "(Optional) Key-value attributes associated with the file"
+                                    {
-                                },
+                                        "type": "boolean"
-                                "file_id": {
+                                    },
-                                    "type": "string",
+                                    {
-                                    "description": "Unique identifier of the file containing the result"
+                                        "type": "number"
-                                },
+                                    },
-                                "filename": {
+                                    {
-                                    "type": "string",
+                                        "type": "string"
-                                    "description": "Name of the file containing the result"
+                                    },
-                                },
+                                    {
-                                "score": {
+                                        "type": "array"
-                                    "type": "number",
+                                    },
-                                    "description": "Relevance score for this search result (between 0 and 1)"
+                                    {
-                                },
+                                        "type": "object"
-                                "text": {
+                                    }
-                                    "type": "string",
+                                ]
-                                    "description": "Text content of the search result"
+                            }
-                                }
-                            },
-                            "additionalProperties": false,
-                            "required": [
-                                "attributes",
-                                "file_id",
-                                "filename",
-                                "score",
-                                "text"
-                            ],
-                            "title": "OpenAIResponseOutputMessageFileSearchToolCallResults",
-                            "description": "Search results returned by the file search operation."
                         },
                         "description": "(Optional) Search results returned by the file search operation"
                     }
@@ -8547,13 +8438,6 @@
                             "$ref": "#/components/schemas/OpenAIResponseInputTool"
                         }
                     },
-                    "include": {
-                        "type": "array",
-                        "items": {
-                            "type": "string"
-                        },
-                        "description": "(Optional) Additional fields to include in the response."
-                    },
                     "max_infer_iters": {
                         "type": "integer"
                     }
@@ -8821,61 +8705,6 @@
                 "title": "OpenAIResponseOutputMessageMCPListTools",
                 "description": "MCP list tools output message containing available tools from an MCP server."
             },
-            "OpenAIResponseContentPart": {
-                "oneOf": [
-                    {
-                        "$ref": "#/components/schemas/OpenAIResponseContentPartOutputText"
-                    },
-                    {
-                        "$ref": "#/components/schemas/OpenAIResponseContentPartRefusal"
-                    }
-                ],
-                "discriminator": {
-                    "propertyName": "type",
-                    "mapping": {
-                        "output_text": "#/components/schemas/OpenAIResponseContentPartOutputText",
-                        "refusal": "#/components/schemas/OpenAIResponseContentPartRefusal"
-                    }
-                }
-            },
-            "OpenAIResponseContentPartOutputText": {
-                "type": "object",
-                "properties": {
-                    "type": {
-                        "type": "string",
-                        "const": "output_text",
-                        "default": "output_text"
-                    },
-                    "text": {
-                        "type": "string"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "type",
-                    "text"
-                ],
-                "title": "OpenAIResponseContentPartOutputText"
-            },
-            "OpenAIResponseContentPartRefusal": {
-                "type": "object",
-                "properties": {
-                    "type": {
-                        "type": "string",
-                        "const": "refusal",
-                        "default": "refusal"
-                    },
-                    "refusal": {
-                        "type": "string"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "type",
-                    "refusal"
-                ],
-                "title": "OpenAIResponseContentPartRefusal"
-            },
             "OpenAIResponseObjectStream": {
                 "oneOf": [
                     {
@@ -8932,12 +8761,6 @@
                     {
                         "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallCompleted"
                     },
-                    {
-                        "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseContentPartAdded"
-                    },
-                    {
-                        "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseContentPartDone"
-                    },
                     {
                         "$ref": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted"
                     }
@@ -8963,8 +8786,6 @@
                         "response.mcp_call.in_progress": "#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallInProgress",
                         "response.mcp_call.failed": "#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallFailed",
                         "response.mcp_call.completed": "#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallCompleted",
-                        "response.content_part.added": "#/components/schemas/OpenAIResponseObjectStreamResponseContentPartAdded",
-                        "response.content_part.done": "#/components/schemas/OpenAIResponseObjectStreamResponseContentPartDone",
                         "response.completed": "#/components/schemas/OpenAIResponseObjectStreamResponseCompleted"
                     }
                 }
@@ -8991,80 +8812,6 @@
                 "title": "OpenAIResponseObjectStreamResponseCompleted",
                 "description": "Streaming event indicating a response has been completed."
             },
-            "OpenAIResponseObjectStreamResponseContentPartAdded": {
-                "type": "object",
-                "properties": {
-                    "response_id": {
-                        "type": "string",
-                        "description": "Unique identifier of the response containing this content"
-                    },
-                    "item_id": {
-                        "type": "string",
-                        "description": "Unique identifier of the output item containing this content part"
-                    },
-                    "part": {
-                        "$ref": "#/components/schemas/OpenAIResponseContentPart",
-                        "description": "The content part that was added"
-                    },
-                    "sequence_number": {
-                        "type": "integer",
-                        "description": "Sequential number for ordering streaming events"
-                    },
-                    "type": {
-                        "type": "string",
-                        "const": "response.content_part.added",
-                        "default": "response.content_part.added",
-                        "description": "Event type identifier, always \"response.content_part.added\""
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "response_id",
-                    "item_id",
-                    "part",
-                    "sequence_number",
-                    "type"
-                ],
-                "title": "OpenAIResponseObjectStreamResponseContentPartAdded",
-                "description": "Streaming event for when a new content part is added to a response item."
-            },
-            "OpenAIResponseObjectStreamResponseContentPartDone": {
-                "type": "object",
-                "properties": {
-                    "response_id": {
-                        "type": "string",
-                        "description": "Unique identifier of the response containing this content"
-                    },
-                    "item_id": {
-                        "type": "string",
-                        "description": "Unique identifier of the output item containing this content part"
-                    },
-                    "part": {
-                        "$ref": "#/components/schemas/OpenAIResponseContentPart",
-                        "description": "The completed content part"
-                    },
-                    "sequence_number": {
-                        "type": "integer",
-                        "description": "Sequential number for ordering streaming events"
-                    },
-                    "type": {
-                        "type": "string",
-                        "const": "response.content_part.done",
-                        "default": "response.content_part.done",
-                        "description": "Event type identifier, always \"response.content_part.done\""
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "response_id",
-                    "item_id",
-                    "part",
-                    "sequence_number",
-                    "type"
-                ],
-                "title": "OpenAIResponseObjectStreamResponseContentPartDone",
-                "description": "Streaming event for when a content part is completed."
-            },
             "OpenAIResponseObjectStreamResponseCreated": {
                 "type": "object",
                 "properties": {
@@ -14767,8 +14514,7 @@
             "OpenAIFilePurpose": {
                 "type": "string",
                 "enum": [
-                    "assistants",
+                    "assistants"
-                    "batch"
                 ],
                 "title": "OpenAIFilePurpose",
                 "description": "Valid purpose values for OpenAI Files API."
@@ -14845,8 +14591,7 @@
                     "purpose": {
                         "type": "string",
                         "enum": [
-                            "assistants",
+                            "assistants"
-                            "batch"
                         ],
                         "description": "The intended purpose of the file"
                     }
@@ -16622,131 +16367,6 @@
                 ],
                 "title": "RunEvalRequest"
             },
-            "RunModerationRequest": {
-                "type": "object",
-                "properties": {
-                    "input": {
-                        "oneOf": [
-                            {
-                                "type": "string"
-                            },
-                            {
-                                "type": "array",
-                                "items": {
-                                    "type": "string"
-                                }
-                            }
-                        ],
-                        "description": "Input (or inputs) to classify. Can be a single string, an array of strings, or an array of multi-modal input objects similar to other models."
-                    },
-                    "model": {
-                        "type": "string",
-                        "description": "The content moderation model you would like to use."
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "input",
-                    "model"
-                ],
-                "title": "RunModerationRequest"
-            },
-            "ModerationObject": {
-                "type": "object",
-                "properties": {
-                    "id": {
-                        "type": "string",
-                        "description": "The unique identifier for the moderation request."
-                    },
-                    "model": {
-                        "type": "string",
-                        "description": "The model used to generate the moderation results."
-                    },
-                    "results": {
-                        "type": "array",
-                        "items": {
-                            "$ref": "#/components/schemas/ModerationObjectResults"
-                        },
-                        "description": "A list of moderation objects"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "id",
-                    "model",
-                    "results"
-                ],
-                "title": "ModerationObject",
-                "description": "A moderation object."
-            },
-            "ModerationObjectResults": {
-                "type": "object",
-                "properties": {
-                    "flagged": {
-                        "type": "boolean",
-                        "description": "Whether any of the below categories are flagged."
-                    },
-                    "categories": {
-                        "type": "object",
-                        "additionalProperties": {
-                            "type": "boolean"
-                        },
-                        "description": "A list of the categories, and whether they are flagged or not."
-                    },
-                    "category_applied_input_types": {
-                        "type": "object",
-                        "additionalProperties": {
-                            "type": "array",
-                            "items": {
-                                "type": "string"
-                            }
-                        },
-                        "description": "A list of the categories along with the input type(s) that the score applies to."
-                    },
-                    "category_scores": {
-                        "type": "object",
-                        "additionalProperties": {
-                            "type": "number"
-                        },
-                        "description": "A list of the categories along with their scores as predicted by model."
-                    },
-                    "user_message": {
-                        "type": "string"
-                    },
-                    "metadata": {
-                        "type": "object",
-                        "additionalProperties": {
-                            "oneOf": [
-                                {
-                                    "type": "null"
-                                },
-                                {
-                                    "type": "boolean"
-                                },
-                                {
-                                    "type": "number"
-                                },
-                                {
-                                    "type": "string"
-                                },
-                                {
-                                    "type": "array"
-                                },
-                                {
-                                    "type": "object"
-                                }
-                            ]
-                        }
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "flagged",
-                    "metadata"
-                ],
-                "title": "ModerationObjectResults",
-                "description": "A moderation object."
-            },
             "RunShieldRequest": {
                 "type": "object",
                 "properties": {

docs/_static/llama-stack-spec.yaml

@@ -999,31 +999,6 @@ paths:
           required: true
           schema:
             type: string
-    delete:
-      responses:
-        '200':
-          description: OK
-        '400':
-          $ref: '#/components/responses/BadRequest400'
-        '429':
-          $ref: >-
-            #/components/responses/TooManyRequests429
-        '500':
-          $ref: >-
-            #/components/responses/InternalServerError500
-        default:
-          $ref: '#/components/responses/DefaultError'
-      tags:
-        - Shields
-      description: Unregister a shield.
-      parameters:
-        - name: identifier
-          in: path
-          description: >-
-            The identifier of the shield to unregister.
-          required: true
-          schema:
-            type: string
   /v1/telemetry/traces/{trace_id}/spans/{span_id}:
     get:
       responses:
@@ -3358,36 +3333,6 @@ paths:
             schema:
               $ref: '#/components/schemas/RunEvalRequest'
         required: true
-  /v1/openai/v1/moderations:
-    post:
-      responses:
-        '200':
-          description: A moderation object.
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/ModerationObject'
-        '400':
-          $ref: '#/components/responses/BadRequest400'
-        '429':
-          $ref: >-
-            #/components/responses/TooManyRequests429
-        '500':
-          $ref: >-
-            #/components/responses/InternalServerError500
-        default:
-          $ref: '#/components/responses/DefaultError'
-      tags:
-        - Safety
-      description: >-
-        Classifies if text and/or image inputs are potentially harmful.
-      parameters: []
-      requestBody:
-        content:
-          application/json:
-            schema:
-              $ref: '#/components/schemas/RunModerationRequest'
-        required: true
   /v1/safety/run-shield:
     post:
       responses:
@@ -6021,44 +5966,14 @@ components:
           type: array
           items:
             type: object
-            properties:
+            additionalProperties:
-              attributes:
+              oneOf:
-                type: object
+                - type: 'null'
-                additionalProperties:
+                - type: boolean
-                  oneOf:
+                - type: number
-                    - type: 'null'
+                - type: string
-                    - type: boolean
+                - type: array
-                    - type: number
+                - type: object
-                    - type: string
-                    - type: array
-                    - type: object
-                description: >-
-                  (Optional) Key-value attributes associated with the file
-              file_id:
-                type: string
-                description: >-
-                  Unique identifier of the file containing the result
-              filename:
-                type: string
-                description: Name of the file containing the result
-              score:
-                type: number
-                description: >-
-                  Relevance score for this search result (between 0 and 1)
-              text:
-                type: string
-                description: Text content of the search result
-            additionalProperties: false
-            required:
-              - attributes
-              - file_id
-              - filename
-              - score
-              - text
-            title: >-
-              OpenAIResponseOutputMessageFileSearchToolCallResults
-            description: >-
-              Search results returned by the file search operation.
           description: >-
             (Optional) Search results returned by the file search operation
       additionalProperties: false
@@ -6218,12 +6133,6 @@ components:
           type: array
           items:
             $ref: '#/components/schemas/OpenAIResponseInputTool'
-        include:
-          type: array
-          items:
-            type: string
-          description: >-
-            (Optional) Additional fields to include in the response.
         max_infer_iters:
           type: integer
       additionalProperties: false
@@ -6441,43 +6350,6 @@ components:
       title: OpenAIResponseOutputMessageMCPListTools
       description: >-
         MCP list tools output message containing available tools from an MCP server.
-    OpenAIResponseContentPart:
-      oneOf:
-        - $ref: '#/components/schemas/OpenAIResponseContentPartOutputText'
-        - $ref: '#/components/schemas/OpenAIResponseContentPartRefusal'
-      discriminator:
-        propertyName: type
-        mapping:
-          output_text: '#/components/schemas/OpenAIResponseContentPartOutputText'
-          refusal: '#/components/schemas/OpenAIResponseContentPartRefusal'
-    OpenAIResponseContentPartOutputText:
-      type: object
-      properties:
-        type:
-          type: string
-          const: output_text
-          default: output_text
-        text:
-          type: string
-      additionalProperties: false
-      required:
-        - type
-        - text
-      title: OpenAIResponseContentPartOutputText
-    OpenAIResponseContentPartRefusal:
-      type: object
-      properties:
-        type:
-          type: string
-          const: refusal
-          default: refusal
-        refusal:
-          type: string
-      additionalProperties: false
-      required:
-        - type
-        - refusal
-      title: OpenAIResponseContentPartRefusal
     OpenAIResponseObjectStream:
       oneOf:
         - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated'
@@ -6498,8 +6370,6 @@ components:
         - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallInProgress'
         - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallFailed'
         - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallCompleted'
-        - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartAdded'
-        - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartDone'
         - $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted'
       discriminator:
         propertyName: type
@@ -6522,8 +6392,6 @@ components:
           response.mcp_call.in_progress: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallInProgress'
           response.mcp_call.failed: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallFailed'
           response.mcp_call.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseMcpCallCompleted'
-          response.content_part.added: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartAdded'
-          response.content_part.done: '#/components/schemas/OpenAIResponseObjectStreamResponseContentPartDone'
           response.completed: '#/components/schemas/OpenAIResponseObjectStreamResponseCompleted'
     "OpenAIResponseObjectStreamResponseCompleted":
       type: object
@@ -6545,76 +6413,6 @@ components:
         OpenAIResponseObjectStreamResponseCompleted
       description: >-
         Streaming event indicating a response has been completed.
-    "OpenAIResponseObjectStreamResponseContentPartAdded":
-      type: object
-      properties:
-        response_id:
-          type: string
-          description: >-
-            Unique identifier of the response containing this content
-        item_id:
-          type: string
-          description: >-
-            Unique identifier of the output item containing this content part
-        part:
-          $ref: '#/components/schemas/OpenAIResponseContentPart'
-          description: The content part that was added
-        sequence_number:
-          type: integer
-          description: >-
-            Sequential number for ordering streaming events
-        type:
-          type: string
-          const: response.content_part.added
-          default: response.content_part.added
-          description: >-
-            Event type identifier, always "response.content_part.added"
-      additionalProperties: false
-      required:
-        - response_id
-        - item_id
-        - part
-        - sequence_number
-        - type
-      title: >-
-        OpenAIResponseObjectStreamResponseContentPartAdded
-      description: >-
-        Streaming event for when a new content part is added to a response item.
-    "OpenAIResponseObjectStreamResponseContentPartDone":
-      type: object
-      properties:
-        response_id:
-          type: string
-          description: >-
-            Unique identifier of the response containing this content
-        item_id:
-          type: string
-          description: >-
-            Unique identifier of the output item containing this content part
-        part:
-          $ref: '#/components/schemas/OpenAIResponseContentPart'
-          description: The completed content part
-        sequence_number:
-          type: integer
-          description: >-
-            Sequential number for ordering streaming events
-        type:
-          type: string
-          const: response.content_part.done
-          default: response.content_part.done
-          description: >-
-            Event type identifier, always "response.content_part.done"
-      additionalProperties: false
-      required:
-        - response_id
-        - item_id
-        - part
-        - sequence_number
-        - type
-      title: >-
-        OpenAIResponseObjectStreamResponseContentPartDone
-      description: >-
-        Streaming event for when a content part is completed.
     "OpenAIResponseObjectStreamResponseCreated":
       type: object
       properties:
@@ -10951,7 +10749,6 @@ components:
       type: string
       enum:
         - assistants
-        - batch
       title: OpenAIFilePurpose
       description: >-
         Valid purpose values for OpenAI Files API.
@@ -11020,7 +10817,6 @@ components:
           type: string
           enum:
             - assistants
-            - batch
           description: The intended purpose of the file
       additionalProperties: false
       required:
@@ -12363,96 +12159,6 @@ components:
       required:
         - benchmark_config
       title: RunEvalRequest
-    RunModerationRequest:
-      type: object
-      properties:
-        input:
-          oneOf:
-            - type: string
-            - type: array
-              items:
-                type: string
-          description: >-
-            Input (or inputs) to classify. Can be a single string, an array of strings,
-            or an array of multi-modal input objects similar to other models.
-        model:
-          type: string
-          description: >-
-            The content moderation model you would like to use.
-      additionalProperties: false
-      required:
-        - input
-        - model
-      title: RunModerationRequest
-    ModerationObject:
-      type: object
-      properties:
-        id:
-          type: string
-          description: >-
-            The unique identifier for the moderation request.
-        model:
-          type: string
-          description: >-
-            The model used to generate the moderation results.
-        results:
-          type: array
-          items:
-            $ref: '#/components/schemas/ModerationObjectResults'
-          description: A list of moderation objects
-      additionalProperties: false
-      required:
-        - id
-        - model
-        - results
-      title: ModerationObject
-      description: A moderation object.
-    ModerationObjectResults:
-      type: object
-      properties:
-        flagged:
-          type: boolean
-          description: >-
-            Whether any of the below categories are flagged.
-        categories:
-          type: object
-          additionalProperties:
-            type: boolean
-          description: >-
-            A list of the categories, and whether they are flagged or not.
-        category_applied_input_types:
-          type: object
-          additionalProperties:
-            type: array
-            items:
-              type: string
-          description: >-
-            A list of the categories along with the input type(s) that the score applies
-            to.
-        category_scores:
-          type: object
-          additionalProperties:
-            type: number
-          description: >-
-            A list of the categories along with their scores as predicted by model.
-        user_message:
-          type: string
-        metadata:
-          type: object
-          additionalProperties:
-            oneOf:
-              - type: 'null'
-              - type: boolean
-              - type: number
-              - type: string
-              - type: array
-              - type: object
-      additionalProperties: false
-      required:
-        - flagged
-        - metadata
-      title: ModerationObjectResults
-      description: A moderation object.
     RunShieldRequest:
       type: object
       properties:

docs/source/apis/external.md

@@ -111,7 +111,7 @@ name = "llama-stack-api-weather"
 version = "0.1.0"
 description = "Weather API for Llama Stack"
 readme = "README.md"
-requires-python = ">=3.12"
+requires-python = ">=3.10"
 dependencies = ["llama-stack", "pydantic"]
 
 [build-system]
@@ -231,7 +231,7 @@ name = "llama-stack-provider-kaze"
 version = "0.1.0"
 description = "Kaze weather provider for Llama Stack"
 readme = "README.md"
-requires-python = ">=3.12"
+requires-python = ">=3.10"
 dependencies = ["llama-stack", "pydantic", "aiohttp"]
 
 [build-system]

docs/source/building_applications/responses_vs_agents.md

@@ -2,9 +2,7 @@
 
 Llama Stack (LLS) provides two different APIs for building AI applications with tool calling capabilities: the **Agents API** and the **OpenAI Responses API**. While both enable AI systems to use tools, and maintain full conversation history, they serve different use cases and have distinct characteristics.
 
-```{note}
+> **Note:** For simple and basic inferencing, you may want to use the [Chat Completions API](https://llama-stack.readthedocs.io/en/latest/providers/index.html#chat-completions) directly, before progressing to Agents or Responses API.
-For simple and basic inferencing, you may want to use the [Chat Completions API](https://llama-stack.readthedocs.io/en/latest/providers/index.html#chat-completions) directly, before progressing to Agents or Responses API.
-```
 
 ## Overview
 

docs/source/building_applications/tools.md

@@ -76,9 +76,7 @@ Features:
 - Context retrieval with token limits
 
 
-```{note}
+> **Note:** By default, llama stack run.yaml defines toolgroups for web search, wolfram alpha and rag, that are provided by tavily-search, wolfram-alpha and rag providers.
-By default, llama stack run.yaml defines toolgroups for web search, wolfram alpha and rag, that are provided by tavily-search, wolfram-alpha and rag providers.
-```
 
 ## Model Context Protocol (MCP)
 

docs/source/concepts/apis.md

@@ -18,4 +18,3 @@ We are working on adding a few more APIs to complete the application lifecycle.
 - **Batch Inference**: run inference on a dataset of inputs
 - **Batch Agents**: run agents on a dataset of inputs
 - **Synthetic Data Generation**: generate synthetic data for model development
-- **Batches**: OpenAI-compatible batch management for inference

docs/source/conf.py

@@ -131,7 +131,6 @@ html_static_path = ["../_static"]
 def setup(app):
     app.add_css_file("css/my_theme.css")
     app.add_js_file("js/detect_theme.js")
-    app.add_js_file("js/keyboard_shortcuts.js")
 
     def dockerhub_role(name, rawtext, text, lineno, inliner, options={}, content=[]):
         url = f"https://hub.docker.com/r/llamastack/{text}"

docs/source/contributing/index.md

@@ -2,38 +2,14 @@
 ```{include} ../../../CONTRIBUTING.md
 ```
 
-## Adding a New Provider
+See the [Adding a New API Provider](new_api_provider.md) which describes how to add new API providers to the Stack.
+
 
-See:
-- [Adding a New API Provider Page](new_api_provider.md) which describes how to add new API providers to the Stack.
-- [Vector Database Page](new_vector_database.md) which describes how to add a new vector databases with Llama Stack.
-- [External Provider Page](../providers/external/index.md) which describes how to add external providers to the Stack.
 
 ```{toctree}
 :maxdepth: 1
 :hidden:
 
 new_api_provider
-new_vector_database
+testing
-```
-
-## Testing
-
-
-```{include} ../../../tests/README.md
-```
-
-## Advanced Topics
-
-For developers who need deeper understanding of the testing system internals:
-
-```{toctree}
-:maxdepth: 1
-
-testing/record-replay
-```
-
-### Benchmarking
-
-```{include} ../../../docs/source/distributions/k8s-benchmark/README.md
 ```

docs/source/contributing/new_vector_database.md

@@ -1,75 +0,0 @@
-# Adding a New Vector Database
-
-This guide will walk you through the process of adding a new vector database to Llama Stack.
-
-> **_NOTE:_** Here's an example Pull Request of the [Milvus Vector Database Provider](https://github.com/meta-llama/llama-stack/pull/1467).
-
-Vector Database providers are used to store and retrieve vector embeddings. Vector databases are not limited to vector
-search but can support keyword and hybrid search. Additionally, vector database can also support operations like
-filtering, sorting, and aggregating vectors.
-
-## Steps to Add a New Vector Database Provider
-1. **Choose the Database Type**: Determine if your vector database is a remote service, inline, or both.
-   - Remote databases make requests to external services, while inline databases execute locally. Some providers support both.
-2. **Implement the Provider**: Create a new provider class that inherits from `VectorDatabaseProvider` and implements the required methods.
-   - Implement methods for vector storage, retrieval, search, and any additional features your database supports.
-     - You will need to implement the following methods for `YourVectorIndex`:
-        - `YourVectorIndex.create()`
-        - `YourVectorIndex.initialize()`
-        - `YourVectorIndex.add_chunks()`
-        - `YourVectorIndex.delete_chunk()`
-        - `YourVectorIndex.query_vector()`
-        - `YourVectorIndex.query_keyword()`
-        - `YourVectorIndex.query_hybrid()`
-     - You will need to implement the following methods for `YourVectorIOAdapter`:
-        - `YourVectorIOAdapter.initialize()`
-        - `YourVectorIOAdapter.shutdown()`
-        - `YourVectorIOAdapter.list_vector_dbs()`
-        - `YourVectorIOAdapter.register_vector_db()`
-        - `YourVectorIOAdapter.unregister_vector_db()`
-        - `YourVectorIOAdapter.insert_chunks()`
-        - `YourVectorIOAdapter.query_chunks()`
-        - `YourVectorIOAdapter.delete_chunks()`
-3. **Add to Registry**: Register your provider in the appropriate registry file.
-   - Update {repopath}`llama_stack/providers/registry/vector_io.py` to include your new provider.
-```python
-from llama_stack.providers.registry.specs import InlineProviderSpec
-from llama_stack.providers.registry.api import Api
-
-InlineProviderSpec(
-    api=Api.vector_io,
-    provider_type="inline::milvus",
-    pip_packages=["pymilvus>=2.4.10"],
-    module="llama_stack.providers.inline.vector_io.milvus",
-    config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig",
-    api_dependencies=[Api.inference],
-    optional_api_dependencies=[Api.files],
-    description="",
-),
-```
-4. **Add Tests**: Create unit tests and integration tests for your provider in the `tests/` directory.
-   - Unit Tests
-     - By following the structure of the class methods, you will be able to easily run unit and integration tests for your database.
-       1. You have to configure the tests for your provide in `/tests/unit/providers/vector_io/conftest.py`.
-       2. Update the `vector_provider` fixture to include your provider if they are an inline provider.
-       3. Create a `your_vectorprovider_index` fixture that initializes your vector index.
-       4. Create a `your_vectorprovider_adapter` fixture that initializes your vector adapter.
-       5. Add your provider to the `vector_io_providers` fixture dictionary.
-         - Please follow the naming convention of `your_vectorprovider_index` and `your_vectorprovider_adapter` as the tests require this to execute properly.
-   - Integration Tests
-     - Integration tests are located in {repopath}`tests/integration`. These tests use the python client-SDK APIs (from the `llama_stack_client` package) to test functionality.
-     - The two set of integration tests are:
-       - `tests/integration/vector_io/test_vector_io.py`: This file tests registration, insertion, and retrieval.
-       - `tests/integration/vector_io/test_openai_vector_stores.py`: These tests are for OpenAI-compatible vector stores and test the OpenAI API compatibility.
-        - You will need to update `skip_if_provider_doesnt_support_openai_vector_stores` to include your provider as well as `skip_if_provider_doesnt_support_openai_vector_stores_search` to test the appropriate search functionality.
-     - Running the tests in the GitHub CI
-       - You will need to update the `.github/workflows/integration-vector-io-tests.yml` file to include your provider.
-        - If your provider is a remote provider, you will also have to add a container to spin up and run it in the action.
-   - Updating the pyproject.yml
-     - If you are adding tests for the `inline` provider you will have to update the `unit` group.
-       - `uv add new_pip_package --group unit`
-     - If you are adding tests for the `remote` provider you will have to update the `test` group, which is used in the GitHub CI for integration tests.
-       - `uv add new_pip_package --group test`
-5. **Update Documentation**: Please update the documentation for end users
-   - Generate the provider documentation by running {repopath}`./scripts/provider_codegen.py`.
-   - Update the autogenerated content in the registry/vector_io.py file with information about your provider. Please see other providers for examples.

docs/source/contributing/testing.md

@@ -0,0 +1,6 @@
+# Testing Llama Stack
+
+Tests are of three different kinds:
+- Unit tests
+- Provider focused integration tests
+- Client SDK tests

docs/source/contributing/testing/record-replay.md

@@ -1,234 +0,0 @@
-# Record-Replay System
-
-Understanding how Llama Stack captures and replays API interactions for testing.
-
-## Overview
-
-The record-replay system solves a fundamental challenge in AI testing: how do you test against expensive, non-deterministic APIs without breaking the bank or dealing with flaky tests?
-
-The solution: intercept API calls, store real responses, and replay them later. This gives you real API behavior without the cost or variability.
-
-## How It Works
-
-### Request Hashing
-
-Every API request gets converted to a deterministic hash for lookup:
-
-```python
-def normalize_request(method: str, url: str, headers: dict, body: dict) -> str:
-    normalized = {
-        "method": method.upper(),
-        "endpoint": urlparse(url).path,  # Just the path, not full URL
-        "body": body,  # Request parameters
-    }
-    return hashlib.sha256(json.dumps(normalized, sort_keys=True).encode()).hexdigest()
-```
-
-**Key insight:** The hashing is intentionally precise. Different whitespace, float precision, or parameter order produces different hashes. This prevents subtle bugs from false cache hits.
-
-```python
-# These produce DIFFERENT hashes:
-{"content": "Hello world"}
-{"content": "Hello   world\n"}
-{"temperature": 0.7}
-{"temperature": 0.7000001}
-```
-
-### Client Interception
-
-The system patches OpenAI and Ollama client methods to intercept calls before they leave your application. This happens transparently - your test code doesn't change.
-
-### Storage Architecture
-
-Recordings use a two-tier storage system optimized for both speed and debuggability:
-
-```
-recordings/
-├── index.sqlite          # Fast lookup by request hash
-└── responses/
-    ├── abc123def456.json  # Individual response files
-    └── def789ghi012.json
-```
-
-**SQLite index** enables O(log n) hash lookups and metadata queries without loading response bodies.
-
-**JSON files** store complete request/response pairs in human-readable format for debugging.
-
-## Recording Modes
-
-### LIVE Mode
-
-Direct API calls with no recording or replay:
-
-```python
-with inference_recording(mode=InferenceMode.LIVE):
-    response = await client.chat.completions.create(...)
-```
-
-Use for initial development and debugging against real APIs.
-
-### RECORD Mode
-
-Captures API interactions while passing through real responses:
-
-```python
-with inference_recording(mode=InferenceMode.RECORD, storage_dir="./recordings"):
-    response = await client.chat.completions.create(...)
-    # Real API call made, response captured AND returned
-```
-
-The recording process:
-1. Request intercepted and hashed
-2. Real API call executed
-3. Response captured and serialized
-4. Recording stored to disk
-5. Original response returned to caller
-
-### REPLAY Mode
-
-Returns stored responses instead of making API calls:
-
-```python
-with inference_recording(mode=InferenceMode.REPLAY, storage_dir="./recordings"):
-    response = await client.chat.completions.create(...)
-    # No API call made, cached response returned instantly
-```
-
-The replay process:
-1. Request intercepted and hashed
-2. Hash looked up in SQLite index
-3. Response loaded from JSON file
-4. Response deserialized and returned
-5. Error if no recording found
-
-## Streaming Support
-
-Streaming APIs present a unique challenge: how do you capture an async generator?
-
-### The Problem
-
-```python
-# How do you record this?
-async for chunk in client.chat.completions.create(stream=True):
-    process(chunk)
-```
-
-### The Solution
-
-The system captures all chunks immediately before yielding any:
-
-```python
-async def handle_streaming_record(response):
-    # Capture complete stream first
-    chunks = []
-    async for chunk in response:
-        chunks.append(chunk)
-
-    # Store complete recording
-    storage.store_recording(
-        request_hash, request_data, {"body": chunks, "is_streaming": True}
-    )
-
-    # Return generator that replays captured chunks
-    async def replay_stream():
-        for chunk in chunks:
-            yield chunk
-
-    return replay_stream()
-```
-
-This ensures:
-- **Complete capture** - The entire stream is saved atomically
-- **Interface preservation** - The returned object behaves like the original API
-- **Deterministic replay** - Same chunks in the same order every time
-
-## Serialization
-
-API responses contain complex Pydantic objects that need careful serialization:
-
-```python
-def _serialize_response(response):
-    if hasattr(response, "model_dump"):
-        # Preserve type information for proper deserialization
-        return {
-            "__type__": f"{response.__class__.__module__}.{response.__class__.__qualname__}",
-            "__data__": response.model_dump(mode="json"),
-        }
-    return response
-```
-
-This preserves type safety - when replayed, you get the same Pydantic objects with all their validation and methods.
-
-## Environment Integration
-
-### Environment Variables
-
-Control recording behavior globally:
-
-```bash
-export LLAMA_STACK_TEST_INFERENCE_MODE=replay
-export LLAMA_STACK_TEST_RECORDING_DIR=/path/to/recordings
-pytest tests/integration/
-```
-
-### Pytest Integration
-
-The system integrates automatically based on environment variables, requiring no changes to test code.
-
-## Debugging Recordings
-
-### Inspecting Storage
-
-```bash
-# See what's recorded
-sqlite3 recordings/index.sqlite "SELECT endpoint, model, timestamp FROM recordings LIMIT 10;"
-
-# View specific response
-cat recordings/responses/abc123def456.json | jq '.response.body'
-
-# Find recordings by endpoint
-sqlite3 recordings/index.sqlite "SELECT * FROM recordings WHERE endpoint='/v1/chat/completions';"
-```
-
-### Common Issues
-
-**Hash mismatches:** Request parameters changed slightly between record and replay
-```bash
-# Compare request details
-cat recordings/responses/abc123.json | jq '.request'
-```
-
-**Serialization errors:** Response types changed between versions
-```bash
-# Re-record with updated types
-rm recordings/responses/failing_hash.json
-LLAMA_STACK_TEST_INFERENCE_MODE=record pytest test_failing.py
-```
-
-**Missing recordings:** New test or changed parameters
-```bash
-# Record the missing interaction
-LLAMA_STACK_TEST_INFERENCE_MODE=record pytest test_new.py
-```
-
-## Design Decisions
-
-### Why Not Mocks?
-
-Traditional mocking breaks down with AI APIs because:
-- Response structures are complex and evolve frequently
-- Streaming behavior is hard to mock correctly
-- Edge cases in real APIs get missed
-- Mocks become brittle maintenance burdens
-
-### Why Precise Hashing?
-
-Loose hashing (normalizing whitespace, rounding floats) seems convenient but hides bugs. If a test changes slightly, you want to know about it rather than accidentally getting the wrong cached response.
-
-### Why JSON + SQLite?
-
-- **JSON** - Human readable, diff-friendly, easy to inspect and modify
-- **SQLite** - Fast indexed lookups without loading response bodies
-- **Hybrid** - Best of both worlds for different use cases
-
-This system provides reliable, fast testing against real AI APIs while maintaining the ability to debug issues when they arise.

docs/source/distributions/building_distro.md

@@ -53,31 +53,24 @@ The main points to consider are:
 
 ```
 llama stack build -h
-usage: llama stack build [-h] [--config CONFIG] [--template TEMPLATE] [--distro DISTRIBUTION] [--list-distros] [--image-type {container,venv}] [--image-name IMAGE_NAME] [--print-deps-only]
+usage: llama stack build [-h] [--config CONFIG] [--template TEMPLATE] [--list-templates] [--image-type {container,venv}] [--image-name IMAGE_NAME] [--print-deps-only] [--run]
-                         [--run] [--providers PROVIDERS]
 
 Build a Llama stack container
 
 options:
   -h, --help            show this help message and exit
-  --config CONFIG       Path to a config file to use for the build. You can find example configs in llama_stack.cores/**/build.yaml. If this argument is not provided, you will be prompted to
+  --config CONFIG       Path to a config file to use for the build. You can find example configs in llama_stack.cores/**/build.yaml. If this argument is not provided, you will
-                        enter information interactively (default: None)
+                        be prompted to enter information interactively (default: None)
-  --template TEMPLATE   (deprecated) Name of the example template config to use for build. You may use `llama stack build --list-distros` to check out the available distributions (default:
+  --template TEMPLATE   Name of the example template config to use for build. You may use `llama stack build --list-templates` to check out the available templates (default: None)
-                        None)
+  --list-templates      Show the available templates for building a Llama Stack distribution (default: False)
-  --distro DISTRIBUTION, --distribution DISTRIBUTION
-                        Name of the distribution to use for build. You may use `llama stack build --list-distros` to check out the available distributions (default: None)
-  --list-distros, --list-distributions
-                        Show the available distributions for building a Llama Stack distribution (default: False)
   --image-type {container,venv}
                         Image Type to use for the build. If not specified, will use the image type from the template config. (default: None)
   --image-name IMAGE_NAME
-                        [for image-type=container|venv] Name of the virtual environment to use for the build. If not specified, currently active environment will be used if found. (default:
+                        [for image-type=container|venv] Name of the virtual environment to use for the build. If not specified, currently active environment will be used if
-                        None)
+                        found. (default: None)
   --print-deps-only     Print the dependencies for the stack only, without building the stack (default: False)
   --run                 Run the stack after building using the same image type, name, and other applicable arguments (default: False)
-  --providers PROVIDERS
+
-                        Build a config for a list of providers and only those providers. This list is formatted like: api1=provider1,api2=provider2. Where there can be multiple providers per
-                        API. (default: None)
 ```
 
 After this step is complete, a file named `<name>-build.yaml` and template file `<name>-run.yaml` will be generated and saved at the output file path specified at the end of the command.

docs/source/distributions/k8s-benchmark/README.md

@@ -1,156 +0,0 @@
-# Llama Stack Benchmark Suite on Kubernetes
-
-## Motivation
-
-Performance benchmarking is critical for understanding the overhead and characteristics of the Llama Stack abstraction layer compared to direct inference engines like vLLM.
-
-### Why This Benchmark Suite Exists
-
-**Performance Validation**: The Llama Stack provides a unified API layer across multiple inference providers, but this abstraction introduces potential overhead. This benchmark suite quantifies the performance impact by comparing:
-- Llama Stack inference (with vLLM backend)
-- Direct vLLM inference calls
-- Both under identical Kubernetes deployment conditions
-
-**Production Readiness Assessment**: Real-world deployments require understanding performance characteristics under load. This suite simulates concurrent user scenarios with configurable parameters (duration, concurrency, request patterns) to validate production readiness.
-
-**Regression Detection (TODO)**: As the Llama Stack evolves, this benchmark provides automated regression detection for performance changes. CI/CD pipelines can leverage these benchmarks to catch performance degradations before production deployments.
-
-**Resource Planning**: By measuring throughput, latency percentiles, and resource utilization patterns, teams can make informed decisions about:
-- Kubernetes resource allocation (CPU, memory, GPU)
-- Auto-scaling configurations
-- Cost optimization strategies
-
-### Key Metrics Captured
-
-The benchmark suite measures critical performance indicators:
-- **Throughput**: Requests per second under sustained load
-- **Latency Distribution**: P50, P95, P99 response times
-- **Time to First Token (TTFT)**: Critical for streaming applications
-- **Error Rates**: Request failures and timeout analysis
-
-This data enables data-driven architectural decisions and performance optimization efforts.
-
-## Setup
-
-**1. Deploy base k8s infrastructure:**
-```bash
-cd ../k8s
-./apply.sh
-```
-
-**2. Deploy benchmark components:**
-```bash
-cd ../k8s-benchmark
-./apply.sh
-```
-
-**3. Verify deployment:**
-```bash
-kubectl get pods
-# Should see: llama-stack-benchmark-server, vllm-server, etc.
-```
-
-## Quick Start
-
-### Basic Benchmarks
-
-**Benchmark Llama Stack (default):**
-```bash
-cd docs/source/distributions/k8s-benchmark/
-./run-benchmark.sh
-```
-
-**Benchmark vLLM direct:**
-```bash
-./run-benchmark.sh --target vllm
-```
-
-### Custom Configuration
-
-**Extended benchmark with high concurrency:**
-```bash
-./run-benchmark.sh --target vllm --duration 120 --concurrent 20
-```
-
-**Short test run:**
-```bash
-./run-benchmark.sh --target stack --duration 30 --concurrent 5
-```
-
-## Command Reference
-
-### run-benchmark.sh Options
-
-```bash
-./run-benchmark.sh [options]
-
-Options:
-  -t, --target <stack|vllm>     Target to benchmark (default: stack)
-  -d, --duration <seconds>      Duration in seconds (default: 60)
-  -c, --concurrent <users>      Number of concurrent users (default: 10)
-  -h, --help                    Show help message
-
-Examples:
-  ./run-benchmark.sh --target vllm              # Benchmark vLLM direct
-  ./run-benchmark.sh --target stack             # Benchmark Llama Stack
-  ./run-benchmark.sh -t vllm -d 120 -c 20       # vLLM with 120s, 20 users
-```
-
-## Local Testing
-
-### Running Benchmark Locally
-
-For local development without Kubernetes:
-
-**1. Start OpenAI mock server:**
-```bash
-uv run python openai-mock-server.py --port 8080
-```
-
-**2. Run benchmark against mock server:**
-```bash
-uv run python benchmark.py \
-  --base-url http://localhost:8080/v1 \
-  --model mock-inference \
-  --duration 30 \
-  --concurrent 5
-```
-
-**3. Test against local vLLM server:**
-```bash
-# If you have vLLM running locally on port 8000
-uv run python benchmark.py \
-  --base-url http://localhost:8000/v1 \
-  --model meta-llama/Llama-3.2-3B-Instruct \
-  --duration 30 \
-  --concurrent 5
-```
-
-**4. Profile the running server:**
-```bash
-./profile_running_server.sh
-```
-
-
-
-### OpenAI Mock Server
-
-The `openai-mock-server.py` provides:
-- **OpenAI-compatible API** for testing without real models
-- **Configurable streaming delay** via `STREAM_DELAY_SECONDS` env var
-- **Consistent responses** for reproducible benchmarks
-- **Lightweight testing** without GPU requirements
-
-**Mock server usage:**
-```bash
-uv run python openai-mock-server.py --port 8080
-```
-
-The mock server is also deployed in k8s as `openai-mock-service:8080` and can be used by changing the Llama Stack configuration to use the `mock-vllm-inference` provider.
-
-## Files in this Directory
-
-- `benchmark.py` - Core benchmark script with async streaming support
-- `run-benchmark.sh` - Main script with target selection and configuration
-- `openai-mock-server.py` - Mock OpenAI API server for local testing
-- `README.md` - This documentation file

docs/source/distributions/k8s-benchmark/apply.sh

@@ -1,36 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-# Deploys the benchmark-specific components on top of the base k8s deployment (../k8s/apply.sh).
-
-export STREAM_DELAY_SECONDS=0.005
-
-export POSTGRES_USER=llamastack
-export POSTGRES_DB=llamastack
-export POSTGRES_PASSWORD=llamastack
-
-export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
-export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
-
-export MOCK_INFERENCE_MODEL=mock-inference
-
-export MOCK_INFERENCE_URL=openai-mock-service:8080
-
-export BENCHMARK_INFERENCE_MODEL=$INFERENCE_MODEL
-
-set -euo pipefail
-set -x
-
-# Deploy benchmark-specific components
-kubectl create configmap llama-stack-config --from-file=stack_run_config.yaml \
-  --dry-run=client -o yaml > stack-configmap.yaml
-
-kubectl apply --validate=false -f stack-configmap.yaml
-
-# Deploy our custom llama stack server (overriding the base one)
-envsubst < stack-k8s.yaml.template | kubectl apply --validate=false -f -

docs/source/distributions/k8s-benchmark/benchmark.py

@@ -1,267 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-"""
-Simple benchmark script for Llama Stack with OpenAI API compatibility.
-"""
-
-import argparse
-import asyncio
-import os
-import random
-import statistics
-import time
-from typing import Tuple
-import aiohttp
-
-
-class BenchmarkStats:
-    def __init__(self):
-        self.response_times = []
-        self.ttft_times = []
-        self.chunks_received = []
-        self.errors = []
-        self.success_count = 0
-        self.total_requests = 0
-        self.concurrent_users = 0
-        self.start_time = None
-        self.end_time = None
-        self._lock = asyncio.Lock()
-
-    async def add_result(self, response_time: float, chunks: int, ttft: float = None, error: str = None):
-        async with self._lock:
-            self.total_requests += 1
-            if error:
-                self.errors.append(error)
-            else:
-                self.success_count += 1
-                self.response_times.append(response_time)
-                self.chunks_received.append(chunks)
-                if ttft is not None:
-                    self.ttft_times.append(ttft)
-
-    def print_summary(self):
-        if not self.response_times:
-            print("No successful requests to report")
-            if self.errors:
-                print(f"Total errors: {len(self.errors)}")
-                print("First 5 errors:")
-                for error in self.errors[:5]:
-                    print(f"  {error}")
-            return
-
-        total_time = self.end_time - self.start_time
-        success_rate = (self.success_count / self.total_requests) * 100
-        
-        print(f"\n{'='*60}")
-        print(f"BENCHMARK RESULTS")
-        print(f"{'='*60}")
-        print(f"Total time: {total_time:.2f}s")
-        print(f"Concurrent users: {self.concurrent_users}")
-        print(f"Total requests: {self.total_requests}")
-        print(f"Successful requests: {self.success_count}")
-        print(f"Failed requests: {len(self.errors)}")
-        print(f"Success rate: {success_rate:.1f}%")
-        print(f"Requests per second: {self.success_count / total_time:.2f}")
-        
-        print(f"\nResponse Time Statistics:")
-        print(f"  Mean: {statistics.mean(self.response_times):.3f}s")
-        print(f"  Median: {statistics.median(self.response_times):.3f}s")
-        print(f"  Min: {min(self.response_times):.3f}s")
-        print(f"  Max: {max(self.response_times):.3f}s")
-        
-        if len(self.response_times) > 1:
-            print(f"  Std Dev: {statistics.stdev(self.response_times):.3f}s")
-            
-        percentiles = [50, 90, 95, 99]
-        sorted_times = sorted(self.response_times)
-        print(f"\nPercentiles:")
-        for p in percentiles:
-            idx = int(len(sorted_times) * p / 100) - 1
-            idx = max(0, min(idx, len(sorted_times) - 1))
-            print(f"  P{p}: {sorted_times[idx]:.3f}s")
-            
-        if self.ttft_times:
-            print(f"\nTime to First Token (TTFT) Statistics:")
-            print(f"  Mean: {statistics.mean(self.ttft_times):.3f}s")
-            print(f"  Median: {statistics.median(self.ttft_times):.3f}s")
-            print(f"  Min: {min(self.ttft_times):.3f}s")
-            print(f"  Max: {max(self.ttft_times):.3f}s")
-            
-            if len(self.ttft_times) > 1:
-                print(f"  Std Dev: {statistics.stdev(self.ttft_times):.3f}s")
-                
-            sorted_ttft = sorted(self.ttft_times)
-            print(f"\nTTFT Percentiles:")
-            for p in percentiles:
-                idx = int(len(sorted_ttft) * p / 100) - 1
-                idx = max(0, min(idx, len(sorted_ttft) - 1))
-                print(f"  P{p}: {sorted_ttft[idx]:.3f}s")
-            
-        if self.chunks_received:
-            print(f"\nStreaming Statistics:")
-            print(f"  Mean chunks per response: {statistics.mean(self.chunks_received):.1f}")
-            print(f"  Total chunks received: {sum(self.chunks_received)}")
-        
-        if self.errors:
-            print(f"\nErrors (showing first 5):")
-            for error in self.errors[:5]:
-                print(f"  {error}")
-
-
-class LlamaStackBenchmark:
-    def __init__(self, base_url: str, model_id: str):
-        self.base_url = base_url.rstrip('/')
-        self.model_id = model_id
-        self.headers = {"Content-Type": "application/json"}
-        self.test_messages = [
-            [{"role": "user", "content": "Hi"}],
-            [{"role": "user", "content": "What is the capital of France?"}],
-            [{"role": "user", "content": "Explain quantum physics in simple terms."}],
-            [{"role": "user", "content": "Write a short story about a robot learning to paint."}],
-            [
-                {"role": "user", "content": "What is machine learning?"},
-                {"role": "assistant", "content": "Machine learning is a subset of AI..."},
-                {"role": "user", "content": "Can you give me a practical example?"}
-            ]
-        ]
-
-
-    async def make_async_streaming_request(self) -> Tuple[float, int, float | None, str | None]:
-        """Make a single async streaming chat completion request."""
-        messages = random.choice(self.test_messages)
-        payload = {
-            "model": self.model_id,
-            "messages": messages,
-            "stream": True,
-            "max_tokens": 100
-        }
-        
-        start_time = time.time()
-        chunks_received = 0
-        ttft = None
-        error = None
-        
-        session = aiohttp.ClientSession()
-        
-        try:
-            async with session.post(
-                f"{self.base_url}/chat/completions",
-                headers=self.headers,
-                json=payload,
-                timeout=aiohttp.ClientTimeout(total=30)
-            ) as response:
-                if response.status == 200:
-                    async for line in response.content:
-                        if line:
-                            line_str = line.decode('utf-8').strip()
-                            if line_str.startswith('data: '):
-                                chunks_received += 1
-                                if ttft is None:
-                                    ttft = time.time() - start_time
-                                if line_str == 'data: [DONE]':
-                                    break
-                    
-                    if chunks_received == 0:
-                        error = "No streaming chunks received"
-                else:
-                    text = await response.text()
-                    error = f"HTTP {response.status}: {text[:100]}"
-                    
-        except Exception as e:
-            error = f"Request error: {str(e)}"
-        finally:
-            await session.close()
-            
-        response_time = time.time() - start_time
-        return response_time, chunks_received, ttft, error
-
-
-    async def run_benchmark(self, duration: int, concurrent_users: int) -> BenchmarkStats:
-        """Run benchmark using async requests for specified duration."""
-        stats = BenchmarkStats()
-        stats.concurrent_users = concurrent_users
-        stats.start_time = time.time()
-        
-        print(f"Starting benchmark: {duration}s duration, {concurrent_users} concurrent users")
-        print(f"Target URL: {self.base_url}/chat/completions")
-        print(f"Model: {self.model_id}")
-        
-        connector = aiohttp.TCPConnector(limit=concurrent_users)
-        async with aiohttp.ClientSession(connector=connector) as session:
-            
-            async def worker(worker_id: int):
-                """Worker that sends requests sequentially until canceled."""
-                request_count = 0
-                while True:
-                    try:
-                        response_time, chunks, ttft, error = await self.make_async_streaming_request()
-                        await stats.add_result(response_time, chunks, ttft, error)
-                        request_count += 1
-                        
-                    except asyncio.CancelledError:
-                        break
-                    except Exception as e:
-                        await stats.add_result(0, 0, None, f"Worker {worker_id} error: {str(e)}")
-            
-            # Progress reporting task
-            async def progress_reporter():
-                last_report_time = time.time()
-                while True:
-                    try:
-                        await asyncio.sleep(1)  # Report every second
-                        if time.time() >= last_report_time + 10:  # Report every 10 seconds
-                            elapsed = time.time() - stats.start_time
-                            print(f"Completed: {stats.total_requests} requests in {elapsed:.1f}s")
-                            last_report_time = time.time()
-                    except asyncio.CancelledError:
-                        break
-            
-            # Spawn concurrent workers
-            tasks = [asyncio.create_task(worker(i)) for i in range(concurrent_users)]
-            progress_task = asyncio.create_task(progress_reporter())
-            tasks.append(progress_task)
-            
-            # Wait for duration then cancel all tasks
-            await asyncio.sleep(duration)
-            
-            for task in tasks:
-                task.cancel()
-            
-            # Wait for all tasks to complete
-            await asyncio.gather(*tasks, return_exceptions=True)
-        
-        stats.end_time = time.time()
-        return stats
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Llama Stack Benchmark Tool")
-    parser.add_argument("--base-url", default=os.getenv("BENCHMARK_BASE_URL", "http://localhost:8000/v1/openai/v1"),
-                       help="Base URL for the API (default: http://localhost:8000/v1/openai/v1)")
-    parser.add_argument("--model", default=os.getenv("INFERENCE_MODEL", "test-model"),
-                       help="Model ID to use for requests")
-    parser.add_argument("--duration", type=int, default=60,
-                       help="Duration in seconds to run benchmark (default: 60)")
-    parser.add_argument("--concurrent", type=int, default=10,
-                       help="Number of concurrent users (default: 10)")
-    
-    args = parser.parse_args()
-    
-    benchmark = LlamaStackBenchmark(args.base_url, args.model)
-    
-    try:
-        stats = asyncio.run(benchmark.run_benchmark(args.duration, args.concurrent))
-        stats.print_summary()
-        
-    except KeyboardInterrupt:
-        print("\nBenchmark interrupted by user")
-    except Exception as e:
-        print(f"Benchmark failed: {e}")
-
-
-if __name__ == "__main__":
-    main()

docs/source/distributions/k8s-benchmark/openai-mock-server.py

@@ -1,190 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-"""
-OpenAI-compatible mock server that returns:
-- Hardcoded /models response for consistent validation
-- Valid OpenAI-formatted chat completion responses with dynamic content
-"""
-
-from flask import Flask, request, jsonify, Response
-import time
-import random
-import uuid
-import json
-import argparse
-import os
-
-app = Flask(__name__)
-
-# Models from environment variables
-def get_models():
-    models_str = os.getenv("MOCK_MODELS", "meta-llama/Llama-3.2-3B-Instruct")
-    model_ids = [m.strip() for m in models_str.split(",") if m.strip()]
-    
-    return {
-        "object": "list",
-        "data": [
-            {
-                "id": model_id,
-                "object": "model",
-                "created": 1234567890,
-                "owned_by": "vllm"
-            }
-            for model_id in model_ids
-        ]
-    }
-
-def generate_random_text(length=50):
-    """Generate random but coherent text for responses."""
-    words = [
-        "Hello", "there", "I'm", "an", "AI", "assistant", "ready", "to", "help", "you",
-        "with", "your", "questions", "and", "tasks", "today", "Let", "me","know", "what",
-        "you'd", "like", "to", "discuss", "or", "explore", "together", "I", "can", "assist",
-        "with", "various", "topics", "including", "coding", "writing", "analysis", "and", "more"
-    ]
-    return " ".join(random.choices(words, k=length))
-
-@app.route('/v1/models', methods=['GET'])
-def list_models():
-    models = get_models()
-    print(f"[MOCK] Returning models: {[m['id'] for m in models['data']]}")
-    return jsonify(models)
-
-@app.route('/v1/chat/completions', methods=['POST'])
-def chat_completions():
-    """Return OpenAI-formatted chat completion responses."""
-    data = request.get_json()
-    default_model = get_models()['data'][0]['id']
-    model = data.get('model', default_model)
-    messages = data.get('messages', [])
-    stream = data.get('stream', False)
-     
-    print(f"[MOCK] Chat completion request - model: {model}, stream: {stream}")
-    
-    if stream:
-        return handle_streaming_completion(model, messages)
-    else:
-        return handle_non_streaming_completion(model, messages)
-
-def handle_non_streaming_completion(model, messages):
-    response_text = generate_random_text(random.randint(20, 80))
-    
-    # Calculate realistic token counts
-    prompt_tokens = sum(len(str(msg.get('content', '')).split()) for msg in messages)
-    completion_tokens = len(response_text.split())
-    
-    response = {
-        "id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
-        "object": "chat.completion",
-        "created": int(time.time()),
-        "model": model,
-        "choices": [
-            {
-                "index": 0,
-                "message": {
-                    "role": "assistant",
-                    "content": response_text
-                },
-                "finish_reason": "stop"
-            }
-        ],
-        "usage": {
-            "prompt_tokens": prompt_tokens,
-            "completion_tokens": completion_tokens,
-            "total_tokens": prompt_tokens + completion_tokens
-        }
-    }
-    
-    return jsonify(response)
-
-def handle_streaming_completion(model, messages):
-    def generate_stream():
-        # Generate response text
-        full_response = generate_random_text(random.randint(30, 100))
-        words = full_response.split()
-        
-        # Send initial chunk
-        initial_chunk = {
-            "id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
-            "object": "chat.completion.chunk",
-            "created": int(time.time()),
-            "model": model,
-            "choices": [
-                {
-                    "index": 0,
-                    "delta": {"role": "assistant", "content": ""}
-                }
-            ]
-        }
-        yield f"data: {json.dumps(initial_chunk)}\n\n"
-        
-        # Send word by word
-        for i, word in enumerate(words):
-            chunk = {
-                "id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
-                "object": "chat.completion.chunk", 
-                "created": int(time.time()),
-                "model": model,
-                "choices": [
-                    {
-                        "index": 0,
-                        "delta": {"content": f"{word} " if i < len(words) - 1 else word}
-                    }
-                ]
-            }
-            yield f"data: {json.dumps(chunk)}\n\n"
-            # Configurable delay to simulate realistic streaming
-            stream_delay = float(os.getenv("STREAM_DELAY_SECONDS", "0.005"))
-            time.sleep(stream_delay)
-        
-        # Send final chunk
-        final_chunk = {
-            "id": f"chatcmpl-{uuid.uuid4().hex[:8]}",
-            "object": "chat.completion.chunk",
-            "created": int(time.time()),
-            "model": model,
-            "choices": [
-                {
-                    "index": 0,
-                    "delta": {"content": ""},
-                    "finish_reason": "stop"
-                }
-            ]
-        }
-        yield f"data: {json.dumps(final_chunk)}\n\n"
-        yield "data: [DONE]\n\n"
-    
-    return Response(
-        generate_stream(),
-        mimetype='text/event-stream',
-        headers={
-            'Cache-Control': 'no-cache',
-            'Connection': 'keep-alive',
-            'Access-Control-Allow-Origin': '*',
-        }
-    )
-
-@app.route('/health', methods=['GET'])
-def health():
-    return jsonify({"status": "healthy", "type": "openai-mock"})
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='OpenAI-compatible mock server')
-    parser.add_argument('--port', type=int, default=8081, 
-                       help='Port to run the server on (default: 8081)')
-    args = parser.parse_args()
-    
-    port = args.port
-    
-    models = get_models()
-    print("Starting OpenAI-compatible mock server...")
-    print(f"- /models endpoint with: {[m['id'] for m in models['data']]}")
-    print("- OpenAI-formatted chat/completion responses with dynamic content")
-    print("- Streaming support with valid SSE format")
-    print(f"- Listening on: http://0.0.0.0:{port}")
-    app.run(host='0.0.0.0', port=port, debug=False)

docs/source/distributions/k8s-benchmark/profile_running_server.sh

@@ -1,52 +0,0 @@
-#!/bin/bash
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-# Script to profile an already running Llama Stack server
-# Usage: ./profile_running_server.sh [duration_seconds] [output_file]
-
-DURATION=${1:-60}  # Default 60 seconds
-OUTPUT_FILE=${2:-"llama_stack_profile"}  # Default output file
-
-echo "Looking for running Llama Stack server..."
-
-# Find the server PID
-SERVER_PID=$(ps aux | grep "llama_stack.core.server.server" | grep -v grep | awk '{print $2}' | head -1)
-
-
-if [ -z "$SERVER_PID" ]; then
-    echo "Error: No running Llama Stack server found"
-    echo "Please start your server first with:"
-    echo "LLAMA_STACK_LOGGING=\"all=ERROR\" MOCK_INFERENCE_URL=http://localhost:8080 SAFETY_MODEL=llama-guard3:1b uv run --with llama-stack python -m llama_stack.core.server.server docs/source/distributions/k8s-benchmark/stack_run_config.yaml"
-    exit 1
-fi
-
-echo "Found Llama Stack server with PID: $SERVER_PID"
-
-# Start py-spy profiling
-echo "Starting py-spy profiling for ${DURATION} seconds..."
-echo "Output will be saved to: ${OUTPUT_FILE}.svg"
-echo ""
-echo "You can now run your load test..."
-echo ""
-
-# Get the full path to py-spy
-PYSPY_PATH=$(which py-spy)
-
-# Check if running as root, if not, use sudo
-if [ "$EUID" -ne 0 ]; then
-    echo "py-spy requires root permissions on macOS. Running with sudo..."
-    sudo "$PYSPY_PATH" record -o "${OUTPUT_FILE}.svg" -d ${DURATION} -p $SERVER_PID
-else
-    "$PYSPY_PATH" record -o "${OUTPUT_FILE}.svg" -d ${DURATION} -p $SERVER_PID
-fi
-
-echo ""
-echo "Profiling completed! Results saved to: ${OUTPUT_FILE}.svg"
-echo ""
-echo "To view the flame graph:"
-echo "open ${OUTPUT_FILE}.svg"

docs/source/distributions/k8s-benchmark/run-benchmark.sh

@@ -1,148 +0,0 @@
-#!/usr/bin/env bash
-
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-set -euo pipefail
-
-# Default values
-TARGET="stack"
-DURATION=60
-CONCURRENT=10
-
-# Parse command line arguments
-usage() {
-    echo "Usage: $0 [options]"
-    echo "Options:"
-    echo "  -t, --target <stack|vllm>     Target to benchmark (default: stack)"
-    echo "  -d, --duration <seconds>      Duration in seconds (default: 60)"
-    echo "  -c, --concurrent <users>      Number of concurrent users (default: 10)"
-    echo "  -h, --help                    Show this help message"
-    echo ""
-    echo "Examples:"
-    echo "  $0 --target vllm              # Benchmark vLLM direct"
-    echo "  $0 --target stack             # Benchmark Llama Stack (default)"
-    echo "  $0 -t vllm -d 120 -c 20       # vLLM with 120s duration, 20 users"
-}
-
-while [[ $# -gt 0 ]]; do
-    case $1 in
-        -t|--target)
-            TARGET="$2"
-            shift 2
-            ;;
-        -d|--duration)
-            DURATION="$2"
-            shift 2
-            ;;
-        -c|--concurrent)
-            CONCURRENT="$2"
-            shift 2
-            ;;
-        -h|--help)
-            usage
-            exit 0
-            ;;
-        *)
-            echo "Unknown option: $1"
-            usage
-            exit 1
-            ;;
-    esac
-done
-
-# Validate target
-if [[ "$TARGET" != "stack" && "$TARGET" != "vllm" ]]; then
-    echo "Error: Target must be 'stack' or 'vllm'"
-    usage
-    exit 1
-fi
-
-# Set configuration based on target
-if [[ "$TARGET" == "vllm" ]]; then
-    BASE_URL="http://vllm-server:8000/v1"
-    JOB_NAME="vllm-benchmark-job"
-    echo "Benchmarking vLLM direct..."
-else
-    BASE_URL="http://llama-stack-benchmark-service:8323/v1/openai/v1"
-    JOB_NAME="stack-benchmark-job"
-    echo "Benchmarking Llama Stack..."
-fi
-
-echo "Configuration:"
-echo "  Target: $TARGET"
-echo "  Base URL: $BASE_URL"
-echo "  Duration: ${DURATION}s"
-echo "  Concurrent users: $CONCURRENT"
-echo ""
-
-# Create temporary job yaml
-TEMP_YAML="/tmp/benchmark-job-temp-$(date +%s).yaml"
-cat > "$TEMP_YAML" << EOF
-apiVersion: batch/v1
-kind: Job
-metadata:
-  name: $JOB_NAME
-  namespace: default
-spec:
-  template:
-    spec:
-      containers:
-      - name: benchmark
-        image: python:3.11-slim
-        command: ["/bin/bash"]
-        args:
-        - "-c"
-        - |
-          pip install aiohttp &&
-          python3 /benchmark/benchmark.py \\
-            --base-url $BASE_URL \\
-            --model \${INFERENCE_MODEL} \\
-            --duration $DURATION \\
-            --concurrent $CONCURRENT
-        env:
-        - name: INFERENCE_MODEL
-          value: "meta-llama/Llama-3.2-3B-Instruct"
-        volumeMounts:
-        - name: benchmark-script
-          mountPath: /benchmark
-        resources:
-          requests:
-            memory: "256Mi"
-            cpu: "250m"
-          limits:
-            memory: "512Mi"
-            cpu: "500m"
-      volumes:
-      - name: benchmark-script
-        configMap:
-          name: benchmark-script
-      restartPolicy: Never
-  backoffLimit: 3
-EOF
-
-echo "Creating benchmark ConfigMap..."
-kubectl create configmap benchmark-script \
-  --from-file=benchmark.py=benchmark.py \
-  --dry-run=client -o yaml | kubectl apply -f -
-
-echo "Cleaning up any existing benchmark job..."
-kubectl delete job $JOB_NAME 2>/dev/null || true
-
-echo "Deploying benchmark Job..."
-kubectl apply -f "$TEMP_YAML"
-
-echo "Waiting for job to start..."
-kubectl wait --for=condition=Ready pod -l job-name=$JOB_NAME --timeout=60s
-
-echo "Following benchmark logs..."
-kubectl logs -f job/$JOB_NAME
-
-echo "Job completed. Checking final status..."
-kubectl get job $JOB_NAME
-
-# Clean up temporary file
-rm -f "$TEMP_YAML"

docs/source/distributions/k8s-benchmark/stack-configmap.yaml

@@ -1,133 +0,0 @@
-apiVersion: v1
-data:
-  stack_run_config.yaml: |
-    version: '2'
-    image_name: kubernetes-benchmark-demo
-    apis:
-    - agents
-    - inference
-    - safety
-    - telemetry
-    - tool_runtime
-    - vector_io
-    providers:
-      inference:
-      - provider_id: vllm-inference
-        provider_type: remote::vllm
-        config:
-          url: ${env.VLLM_URL:=http://localhost:8000/v1}
-          max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
-          api_token: ${env.VLLM_API_TOKEN:=fake}
-          tls_verify: ${env.VLLM_TLS_VERIFY:=true}
-      - provider_id: vllm-safety
-        provider_type: remote::vllm
-        config:
-          url: ${env.VLLM_SAFETY_URL:=http://localhost:8000/v1}
-          max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
-          api_token: ${env.VLLM_API_TOKEN:=fake}
-          tls_verify: ${env.VLLM_TLS_VERIFY:=true}
-      - provider_id: sentence-transformers
-        provider_type: inline::sentence-transformers
-        config: {}
-      vector_io:
-      - provider_id: ${env.ENABLE_CHROMADB:+chromadb}
-        provider_type: remote::chromadb
-        config:
-          url: ${env.CHROMADB_URL:=}
-          kvstore:
-            type: postgres
-            host: ${env.POSTGRES_HOST:=localhost}
-            port: ${env.POSTGRES_PORT:=5432}
-            db: ${env.POSTGRES_DB:=llamastack}
-            user: ${env.POSTGRES_USER:=llamastack}
-            password: ${env.POSTGRES_PASSWORD:=llamastack}
-      safety:
-      - provider_id: llama-guard
-        provider_type: inline::llama-guard
-        config:
-          excluded_categories: []
-      agents:
-      - provider_id: meta-reference
-        provider_type: inline::meta-reference
-        config:
-          persistence_store:
-            type: postgres
-            host: ${env.POSTGRES_HOST:=localhost}
-            port: ${env.POSTGRES_PORT:=5432}
-            db: ${env.POSTGRES_DB:=llamastack}
-            user: ${env.POSTGRES_USER:=llamastack}
-            password: ${env.POSTGRES_PASSWORD:=llamastack}
-          responses_store:
-            type: postgres
-            host: ${env.POSTGRES_HOST:=localhost}
-            port: ${env.POSTGRES_PORT:=5432}
-            db: ${env.POSTGRES_DB:=llamastack}
-            user: ${env.POSTGRES_USER:=llamastack}
-            password: ${env.POSTGRES_PASSWORD:=llamastack}
-      telemetry:
-      - provider_id: meta-reference
-        provider_type: inline::meta-reference
-        config:
-          service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
-          sinks: ${env.TELEMETRY_SINKS:=console}
-      tool_runtime:
-      - provider_id: brave-search
-        provider_type: remote::brave-search
-        config:
-          api_key: ${env.BRAVE_SEARCH_API_KEY:+}
-          max_results: 3
-      - provider_id: tavily-search
-        provider_type: remote::tavily-search
-        config:
-          api_key: ${env.TAVILY_SEARCH_API_KEY:+}
-          max_results: 3
-      - provider_id: rag-runtime
-        provider_type: inline::rag-runtime
-        config: {}
-      - provider_id: model-context-protocol
-        provider_type: remote::model-context-protocol
-        config: {}
-    metadata_store:
-      type: postgres
-      host: ${env.POSTGRES_HOST:=localhost}
-      port: ${env.POSTGRES_PORT:=5432}
-      db: ${env.POSTGRES_DB:=llamastack}
-      user: ${env.POSTGRES_USER:=llamastack}
-      password: ${env.POSTGRES_PASSWORD:=llamastack}
-      table_name: llamastack_kvstore
-    inference_store:
-      type: postgres
-      host: ${env.POSTGRES_HOST:=localhost}
-      port: ${env.POSTGRES_PORT:=5432}
-      db: ${env.POSTGRES_DB:=llamastack}
-      user: ${env.POSTGRES_USER:=llamastack}
-      password: ${env.POSTGRES_PASSWORD:=llamastack}
-    models:
-    - metadata:
-        embedding_dimension: 384
-      model_id: all-MiniLM-L6-v2
-      provider_id: sentence-transformers
-      model_type: embedding
-    - model_id: ${env.INFERENCE_MODEL}
-      provider_id: vllm-inference
-      model_type: llm
-    - model_id: ${env.SAFETY_MODEL}
-      provider_id: vllm-safety
-      model_type: llm
-    shields:
-    - shield_id: ${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-1B}
-    vector_dbs: []
-    datasets: []
-    scoring_fns: []
-    benchmarks: []
-    tool_groups:
-    - toolgroup_id: builtin::websearch
-      provider_id: tavily-search
-    - toolgroup_id: builtin::rag
-      provider_id: rag-runtime
-    server:
-      port: 8323
-kind: ConfigMap
-metadata:
-  creationTimestamp: null
-  name: llama-stack-config

docs/source/distributions/k8s-benchmark/stack-k8s.yaml.template

@@ -1,83 +0,0 @@
-apiVersion: v1
-kind: PersistentVolumeClaim
-metadata:
-  name: llama-benchmark-pvc
-spec:
-  accessModes:
-    - ReadWriteOnce
-  resources:
-    requests:
-      storage: 1Gi
----
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: llama-stack-benchmark-server
-spec:
-  replicas: 1
-  selector:
-    matchLabels:
-      app.kubernetes.io/name: llama-stack-benchmark
-      app.kubernetes.io/component: server
-  template:
-    metadata:
-      labels:
-        app.kubernetes.io/name: llama-stack-benchmark
-        app.kubernetes.io/component: server
-    spec:
-      containers:
-      - name: llama-stack-benchmark
-        image: llamastack/distribution-starter:latest
-        imagePullPolicy: Always # since we have specified latest instead of a version
-        env:
-        - name: ENABLE_CHROMADB
-          value: "true"
-        - name: CHROMADB_URL
-          value: http://chromadb.default.svc.cluster.local:6000
-        - name: POSTGRES_HOST
-          value: postgres-server.default.svc.cluster.local
-        - name: POSTGRES_PORT
-          value: "5432"
-        - name: INFERENCE_MODEL
-          value: "${INFERENCE_MODEL}"
-        - name: SAFETY_MODEL
-          value: "${SAFETY_MODEL}"
-        - name: TAVILY_SEARCH_API_KEY
-          value: "${TAVILY_SEARCH_API_KEY}"
-        - name: VLLM_URL
-          value: http://vllm-server.default.svc.cluster.local:8000/v1
-        - name: VLLM_MAX_TOKENS
-          value: "3072"
-        - name: VLLM_SAFETY_URL
-          value: http://vllm-server-safety.default.svc.cluster.local:8001/v1
-        - name: VLLM_TLS_VERIFY
-          value: "false"
-        command: ["python", "-m", "llama_stack.core.server.server", "/etc/config/stack_run_config.yaml", "--port", "8323"]
-        ports:
-          - containerPort: 8323
-        volumeMounts:
-          - name: llama-storage
-            mountPath: /root/.llama
-          - name: llama-config
-            mountPath: /etc/config
-      volumes:
-      - name: llama-storage
-        persistentVolumeClaim:
-          claimName: llama-benchmark-pvc
-      - name: llama-config
-        configMap:
-          name: llama-stack-config
----
-apiVersion: v1
-kind: Service
-metadata:
-  name: llama-stack-benchmark-service
-spec:
-  selector:
-    app.kubernetes.io/name: llama-stack-benchmark
-    app.kubernetes.io/component: server
-  ports:
-  - name: http
-    port: 8323
-    targetPort: 8323
-  type: ClusterIP

docs/source/distributions/k8s-benchmark/stack_run_config.yaml

@@ -1,108 +0,0 @@
-version: '2'
-image_name: kubernetes-benchmark-demo
-apis:
-- agents
-- inference
-- telemetry
-- tool_runtime
-- vector_io
-providers:
-  inference:
-  - provider_id: vllm-inference
-    provider_type: remote::vllm
-    config:
-      url: ${env.VLLM_URL:=http://localhost:8000/v1}
-      max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
-      api_token: ${env.VLLM_API_TOKEN:=fake}
-      tls_verify: ${env.VLLM_TLS_VERIFY:=true}
-  - provider_id: sentence-transformers
-    provider_type: inline::sentence-transformers
-    config: {}
-  vector_io:
-  - provider_id: ${env.ENABLE_CHROMADB:+chromadb}
-    provider_type: remote::chromadb
-    config:
-      url: ${env.CHROMADB_URL:=}
-      kvstore:
-        type: postgres
-        host: ${env.POSTGRES_HOST:=localhost}
-        port: ${env.POSTGRES_PORT:=5432}
-        db: ${env.POSTGRES_DB:=llamastack}
-        user: ${env.POSTGRES_USER:=llamastack}
-        password: ${env.POSTGRES_PASSWORD:=llamastack}
-  agents:
-  - provider_id: meta-reference
-    provider_type: inline::meta-reference
-    config:
-      persistence_store:
-        type: postgres
-        host: ${env.POSTGRES_HOST:=localhost}
-        port: ${env.POSTGRES_PORT:=5432}
-        db: ${env.POSTGRES_DB:=llamastack}
-        user: ${env.POSTGRES_USER:=llamastack}
-        password: ${env.POSTGRES_PASSWORD:=llamastack}
-      responses_store:
-        type: postgres
-        host: ${env.POSTGRES_HOST:=localhost}
-        port: ${env.POSTGRES_PORT:=5432}
-        db: ${env.POSTGRES_DB:=llamastack}
-        user: ${env.POSTGRES_USER:=llamastack}
-        password: ${env.POSTGRES_PASSWORD:=llamastack}
-  telemetry:
-  - provider_id: meta-reference
-    provider_type: inline::meta-reference
-    config:
-      service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
-      sinks: ${env.TELEMETRY_SINKS:=console}
-  tool_runtime:
-  - provider_id: brave-search
-    provider_type: remote::brave-search
-    config:
-      api_key: ${env.BRAVE_SEARCH_API_KEY:+}
-      max_results: 3
-  - provider_id: tavily-search
-    provider_type: remote::tavily-search
-    config:
-      api_key: ${env.TAVILY_SEARCH_API_KEY:+}
-      max_results: 3
-  - provider_id: rag-runtime
-    provider_type: inline::rag-runtime
-    config: {}
-  - provider_id: model-context-protocol
-    provider_type: remote::model-context-protocol
-    config: {}
-metadata_store:
-  type: postgres
-  host: ${env.POSTGRES_HOST:=localhost}
-  port: ${env.POSTGRES_PORT:=5432}
-  db: ${env.POSTGRES_DB:=llamastack}
-  user: ${env.POSTGRES_USER:=llamastack}
-  password: ${env.POSTGRES_PASSWORD:=llamastack}
-  table_name: llamastack_kvstore
-inference_store:
-  type: postgres
-  host: ${env.POSTGRES_HOST:=localhost}
-  port: ${env.POSTGRES_PORT:=5432}
-  db: ${env.POSTGRES_DB:=llamastack}
-  user: ${env.POSTGRES_USER:=llamastack}
-  password: ${env.POSTGRES_PASSWORD:=llamastack}
-models:
-- metadata:
-    embedding_dimension: 384
-  model_id: all-MiniLM-L6-v2
-  provider_id: sentence-transformers
-  model_type: embedding
-- model_id: ${env.INFERENCE_MODEL}
-  provider_id: vllm-inference
-  model_type: llm
-vector_dbs: []
-datasets: []
-scoring_fns: []
-benchmarks: []
-tool_groups:
-- toolgroup_id: builtin::websearch
-  provider_id: tavily-search
-- toolgroup_id: builtin::rag
-  provider_id: rag-runtime
-server:
-  port: 8323

docs/source/distributions/k8s/stack-k8s.yaml.template

@@ -40,19 +40,19 @@ spec:
           value: "3072"
         - name: VLLM_SAFETY_URL
           value: http://vllm-server-safety.default.svc.cluster.local:8001/v1
-        - name: VLLM_TLS_VERIFY
-          value: "false"
         - name: POSTGRES_HOST
           value: postgres-server.default.svc.cluster.local
         - name: POSTGRES_PORT
           value: "5432"
+        - name: VLLM_TLS_VERIFY
+          value: "false"
         - name: INFERENCE_MODEL
           value: "${INFERENCE_MODEL}"
         - name: SAFETY_MODEL
           value: "${SAFETY_MODEL}"
         - name: TAVILY_SEARCH_API_KEY
           value: "${TAVILY_SEARCH_API_KEY}"
-        command: ["python", "-m", "llama_stack.core.server.server", "/etc/config/stack_run_config.yaml", "--port", "8321"]
+        command: ["python", "-m", "llama_stack.core.server.server", "--config", "/etc/config/stack_run_config.yaml", "--port", "8321"]
         ports:
           - containerPort: 8321
         volumeMounts:

docs/source/distributions/ondevice_distro/android_sdk.md

@@ -56,12 +56,12 @@ Breaking down the demo app, this section will show the core pieces that are used
 ### Setup Remote Inferencing
 Start a Llama Stack server on localhost. Here is an example of how you can do this using the firework.ai distribution:
 ```
-uv venv starter --python 3.12
+python -m venv stack-fireworks
-source starter/bin/activate  # On Windows: starter\Scripts\activate
+source stack-fireworks/bin/activate  # On Windows: stack-fireworks\Scripts\activate
 pip install --no-cache llama-stack==0.2.2
-llama stack build --distro starter --image-type venv
+llama stack build --distro fireworks --image-type venv
 export FIREWORKS_API_KEY=<SOME_KEY>
-llama stack run starter --port 5050
+llama stack run fireworks --port 5050
 ```
 
 Ensure the Llama Stack server version is the same as the Kotlin SDK Library for maximum compatibility.

docs/source/distributions/self_hosted_distro/nvidia.md

@@ -157,7 +157,7 @@ docker run \
 If you've set up your local development environment, you can also build the image using your local virtual environment.
 
 ```bash
-INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct
+INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct
 llama stack build --distro nvidia --image-type venv
 llama stack run ./run.yaml \
   --port 8321 \

docs/source/getting_started/demo_script.py

@@ -52,16 +52,11 @@ agent = Agent(
 prompt = "How do you do great work?"
 print("prompt>", prompt)
 
-use_stream = True
 response = agent.create_turn(
     messages=[{"role": "user", "content": prompt}],
     session_id=agent.create_session("rag_session"),
-    stream=use_stream,
+    stream=True,
 )
 
-# Only call `AgentEventLogger().log(response)` for streaming responses.
+for log in AgentEventLogger().log(response):
-if use_stream:
+    log.print()
-    for log in AgentEventLogger().log(response):
-        log.print()
-else:
-    print(response)

docs/source/getting_started/detailed_tutorial.md

@@ -150,7 +150,13 @@ pip install llama-stack-client
 ```
 :::
 
-
+:::{tab-item} Install with `venv`
+```bash
+python -m venv stack-client
+source stack-client/bin/activate  # On Windows: stack-client\Scripts\activate
+pip install llama-stack-client
+```
+:::
 ::::
 
 Now let's use the `llama-stack-client` [CLI](../references/llama_stack_client_cli_reference.md) to check the

docs/source/providers/agents/index.md

@@ -2,15 +2,6 @@
 
 ## Overview
 
-Agents API for creating and interacting with agentic systems.
-
-    Main functionalities provided by this API:
-    - Create agents with specific instructions and ability to use tools.
-    - Interactions with agents are grouped into sessions ("threads"), and each interaction is called a "turn".
-    - Agents can be provided with various tools (see the ToolGroups and ToolRuntime APIs for more details).
-    - Agents can be provided with various shields (see the Safety API for more details).
-    - Agents can also use Memory to retrieve information from knowledge bases. See the RAG Tool and Vector IO APIs for more details.
-
 This section contains documentation for all available providers for the **agents** API.
 
 ## Providers

docs/source/providers/batches/index.md

@@ -1,21 +0,0 @@
-# Batches
-
-## Overview
-
-Protocol for batch processing API operations.
-
-    The Batches API enables efficient processing of multiple requests in a single operation,
-    particularly useful for processing large datasets, batch evaluation workflows, and
-    cost-effective inference at scale.
-
-    Note: This API is currently under active development and may undergo changes.
-
-This section contains documentation for all available providers for the **batches** API.
-
-## Providers
-
-```{toctree}
-:maxdepth: 1
-
-inline_reference
-```

docs/source/providers/batches/inline_reference.md

@@ -1,23 +0,0 @@
-# inline::reference
-
-## Description
-
-Reference implementation of batches API with KVStore persistence.
-
-## Configuration
-
-| Field | Type | Required | Default | Description |
-|-------|------|----------|---------|-------------|
-| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Configuration for the key-value store backend. |
-| `max_concurrent_batches` | `<class 'int'>` | No | 1 | Maximum number of concurrent batches to process simultaneously. |
-| `max_concurrent_requests_per_batch` | `<class 'int'>` | No | 10 | Maximum number of concurrent requests to process per batch. |
-
-## Sample Configuration
-
-```yaml
-kvstore:
-  type: sqlite
-  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/batches.db
-
-```
-

docs/source/providers/eval/index.md

@@ -2,8 +2,6 @@
 
 ## Overview
 
-Llama Stack Evaluation API for running evaluations on model and agent candidates.
-
 This section contains documentation for all available providers for the **eval** API.
 
 ## Providers

docs/source/providers/external/external-providers-guide.md

@@ -226,7 +226,7 @@ uv init
 name = "llama-stack-provider-ollama"
 version = "0.1.0"
 description = "Ollama provider for Llama Stack"
-requires-python = ">=3.12"
+requires-python = ">=3.10"
 dependencies = ["llama-stack", "pydantic", "ollama", "aiohttp"]
 ```
 

docs/source/providers/files/inline_localfs.md

@@ -8,7 +8,7 @@ Local filesystem-based file storage provider for managing files and documents lo
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `storage_dir` | `<class 'str'>` | No |  | Directory to store uploaded files |
+| `storage_dir` | `<class 'str'>` | No | PydanticUndefined | Directory to store uploaded files |
 | `metadata_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite | SQL store configuration for file metadata |
 | `ttl_secs` | `<class 'int'>` | No | 31536000 |  |
 

docs/source/providers/inference/index.md

@@ -2,12 +2,6 @@
 
 ## Overview
 
-Llama Stack Inference API for generating completions, chat completions, and embeddings.
-
-    This API provides the raw interface to the underlying models. Two kinds of models are supported:
-    - LLM models: these models generate "raw" and "chat" (conversational) completions.
-    - Embedding models: these models generate embeddings to be used for semantic search.
-
 This section contains documentation for all available providers for the **inference** API.
 
 ## Providers
@@ -35,7 +29,6 @@ remote_runpod
 remote_sambanova
 remote_tgi
 remote_together
-remote_vertexai
 remote_vllm
 remote_watsonx
 ```

docs/source/providers/inference/remote_hf_endpoint.md

@@ -8,7 +8,7 @@ HuggingFace Inference Endpoints provider for dedicated model serving.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `endpoint_name` | `<class 'str'>` | No |  | The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided. |
+| `endpoint_name` | `<class 'str'>` | No | PydanticUndefined | The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided. |
 | `api_token` | `pydantic.types.SecretStr \| None` | No |  | Your Hugging Face user access token (will default to locally saved token if not provided) |
 
 ## Sample Configuration

docs/source/providers/inference/remote_hf_serverless.md

@@ -8,7 +8,7 @@ HuggingFace Inference API serverless provider for on-demand model inference.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `huggingface_repo` | `<class 'str'>` | No |  | The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct') |
+| `huggingface_repo` | `<class 'str'>` | No | PydanticUndefined | The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct') |
 | `api_token` | `pydantic.types.SecretStr \| None` | No |  | Your Hugging Face user access token (will default to locally saved token if not provided) |
 
 ## Sample Configuration

docs/source/providers/inference/remote_tgi.md

@@ -8,7 +8,7 @@ Text Generation Inference (TGI) provider for HuggingFace model serving.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `url` | `<class 'str'>` | No |  | The URL for the TGI serving endpoint |
+| `url` | `<class 'str'>` | No | PydanticUndefined | The URL for the TGI serving endpoint |
 
 ## Sample Configuration
 

docs/source/providers/inference/remote_vertexai.md

@@ -1,40 +0,0 @@
-# remote::vertexai
-
-## Description
-
-Google Vertex AI inference provider enables you to use Google's Gemini models through Google Cloud's Vertex AI platform, providing several advantages:
-
-• Enterprise-grade security: Uses Google Cloud's security controls and IAM
-• Better integration: Seamless integration with other Google Cloud services
-• Advanced features: Access to additional Vertex AI features like model tuning and monitoring
-• Authentication: Uses Google Cloud Application Default Credentials (ADC) instead of API keys
-
-Configuration:
-- Set VERTEX_AI_PROJECT environment variable (required)
-- Set VERTEX_AI_LOCATION environment variable (optional, defaults to us-central1)
-- Use Google Cloud Application Default Credentials or service account key
-
-Authentication Setup:
-Option 1 (Recommended): gcloud auth application-default login
-Option 2: Set GOOGLE_APPLICATION_CREDENTIALS to service account key path
-
-Available Models:
-- vertex_ai/gemini-2.0-flash
-- vertex_ai/gemini-2.5-flash
-- vertex_ai/gemini-2.5-pro
-
-## Configuration
-
-| Field | Type | Required | Default | Description |
-|-------|------|----------|---------|-------------|
-| `project` | `<class 'str'>` | No |  | Google Cloud project ID for Vertex AI |
-| `location` | `<class 'str'>` | No | us-central1 | Google Cloud location for Vertex AI |
-
-## Sample Configuration
-
-```yaml
-project: ${env.VERTEX_AI_PROJECT:=}
-location: ${env.VERTEX_AI_LOCATION:=us-central1}
-
-```
-

docs/source/providers/post_training/inline_huggingface.md

@@ -27,7 +27,7 @@ HuggingFace-based post-training provider for fine-tuning models using the Huggin
 | `dpo_beta` | `<class 'float'>` | No | 0.1 |  |
 | `use_reference_model` | `<class 'bool'>` | No | True |  |
 | `dpo_loss_type` | `Literal['sigmoid', 'hinge', 'ipo', 'kto_pair'` | No | sigmoid |  |
-| `dpo_output_dir` | `<class 'str'>` | No |  |  |
+| `dpo_output_dir` | `<class 'str'>` | No | ./checkpoints/dpo |  |
 
 ## Sample Configuration
 
@@ -35,7 +35,6 @@ HuggingFace-based post-training provider for fine-tuning models using the Huggin
 checkpoint_format: huggingface
 distributed_backend: null
 device: cpu
-dpo_output_dir: ~/.llama/dummy/dpo_output
 
 ```
 

docs/source/providers/vector_io/inline_chromadb.md

@@ -41,7 +41,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `db_path` | `<class 'str'>` | No |  |  |
+| `db_path` | `<class 'str'>` | No | PydanticUndefined |  |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
 
 ## Sample Configuration

docs/source/providers/vector_io/inline_faiss.md

@@ -12,18 +12,6 @@ That means you'll get fast and efficient vector retrieval.
 - Lightweight and easy to use
 - Fully integrated with Llama Stack
 - GPU support
-- **Vector search** - FAISS supports pure vector similarity search using embeddings
-
-## Search Modes
-
-**Supported:**
-- **Vector Search** (`mode="vector"`): Performs vector similarity search using embeddings
-
-**Not Supported:**
-- **Keyword Search** (`mode="keyword"`): Not supported by FAISS
-- **Hybrid Search** (`mode="hybrid"`): Not supported by FAISS
-
-> **Note**: FAISS is designed as a pure vector similarity search library. See the [FAISS GitHub repository](https://github.com/facebookresearch/faiss) for more details about FAISS's core functionality.
 
 ## Usage
 

docs/source/providers/vector_io/inline_meta-reference.md

@@ -21,7 +21,5 @@ kvstore:
 
 ## Deprecation Notice
 
-```{warning}
+⚠️ **Warning**: Please use the `inline::faiss` provider instead.
-Please use the `inline::faiss` provider instead.
-```
 

docs/source/providers/vector_io/inline_milvus.md

@@ -10,7 +10,7 @@ Please refer to the remote provider documentation.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `db_path` | `<class 'str'>` | No |  |  |
+| `db_path` | `<class 'str'>` | No | PydanticUndefined |  |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
 | `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
 

docs/source/providers/vector_io/inline_qdrant.md

@@ -50,7 +50,7 @@ See the [Qdrant documentation](https://qdrant.tech/documentation/) for more deta
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `path` | `<class 'str'>` | No |  |  |
+| `path` | `<class 'str'>` | No | PydanticUndefined |  |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
 
 ## Sample Configuration

docs/source/providers/vector_io/inline_sqlite-vec.md

@@ -205,7 +205,7 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `db_path` | `<class 'str'>` | No |  | Path to the SQLite database file |
+| `db_path` | `<class 'str'>` | No | PydanticUndefined | Path to the SQLite database file |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
 
 ## Sample Configuration

docs/source/providers/vector_io/inline_sqlite_vec.md

@@ -10,7 +10,7 @@ Please refer to the sqlite-vec provider documentation.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `db_path` | `<class 'str'>` | No |  | Path to the SQLite database file |
+| `db_path` | `<class 'str'>` | No | PydanticUndefined | Path to the SQLite database file |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
 
 ## Sample Configuration
@@ -25,7 +25,5 @@ kvstore:
 
 ## Deprecation Notice
 
-```{warning}
+⚠️ **Warning**: Please use the `inline::sqlite-vec` provider (notice the hyphen instead of underscore) instead.
-Please use the `inline::sqlite-vec` provider (notice the hyphen instead of underscore) instead.
-```
 

docs/source/providers/vector_io/remote_chromadb.md

@@ -40,7 +40,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `url` | `str \| None` | No |  |  |
+| `url` | `str \| None` | No | PydanticUndefined |  |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
 
 ## Sample Configuration

docs/source/providers/vector_io/remote_milvus.md

@@ -11,7 +11,6 @@ That means you're not limited to storing vectors in memory or in a separate serv
 
 - Easy to use
 - Fully integrated with Llama Stack
-- Supports all search modes: vector, keyword, and hybrid search (both inline and remote configurations)
 
 ## Usage
 
@@ -102,92 +101,6 @@ vector_io:
 - **`client_pem_path`**: Path to the **client certificate** file (required for mTLS).
 - **`client_key_path`**: Path to the **client private key** file (required for mTLS).
 
-## Search Modes
-
-Milvus supports three different search modes for both inline and remote configurations:
-
-### Vector Search
-Vector search uses semantic similarity to find the most relevant chunks based on embedding vectors. This is the default search mode and works well for finding conceptually similar content.
-
-```python
-# Vector search example
-search_response = client.vector_stores.search(
-    vector_store_id=vector_store.id,
-    query="What is machine learning?",
-    search_mode="vector",
-    max_num_results=5,
-)
-```
-
-### Keyword Search
-Keyword search uses traditional text-based matching to find chunks containing specific terms or phrases. This is useful when you need exact term matches.
-
-```python
-# Keyword search example
-search_response = client.vector_stores.search(
-    vector_store_id=vector_store.id,
-    query="Python programming language",
-    search_mode="keyword",
-    max_num_results=5,
-)
-```
-
-### Hybrid Search
-Hybrid search combines both vector and keyword search methods to provide more comprehensive results. It leverages the strengths of both semantic similarity and exact term matching.
-
-#### Basic Hybrid Search
-```python
-# Basic hybrid search example (uses RRF ranker with default impact_factor=60.0)
-search_response = client.vector_stores.search(
-    vector_store_id=vector_store.id,
-    query="neural networks in Python",
-    search_mode="hybrid",
-    max_num_results=5,
-)
-```
-
-**Note**: The default `impact_factor` value of 60.0 was empirically determined to be optimal in the original RRF research paper: ["Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods"](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) (Cormack et al., 2009).
-
-#### Hybrid Search with RRF (Reciprocal Rank Fusion) Ranker
-RRF combines rankings from vector and keyword search by using reciprocal ranks. The impact factor controls how much weight is given to higher-ranked results.
-
-```python
-# Hybrid search with custom RRF parameters
-search_response = client.vector_stores.search(
-    vector_store_id=vector_store.id,
-    query="neural networks in Python",
-    search_mode="hybrid",
-    max_num_results=5,
-    ranking_options={
-        "ranker": {
-            "type": "rrf",
-            "impact_factor": 100.0,  # Higher values give more weight to top-ranked results
-        }
-    },
-)
-```
-
-#### Hybrid Search with Weighted Ranker
-Weighted ranker linearly combines normalized scores from vector and keyword search. The alpha parameter controls the balance between the two search methods.
-
-```python
-# Hybrid search with weighted ranker
-search_response = client.vector_stores.search(
-    vector_store_id=vector_store.id,
-    query="neural networks in Python",
-    search_mode="hybrid",
-    max_num_results=5,
-    ranking_options={
-        "ranker": {
-            "type": "weighted",
-            "alpha": 0.7,  # 70% vector search, 30% keyword search
-        }
-    },
-)
-```
-
-For detailed documentation on RRF and Weighted rankers, please refer to the [Milvus Reranking Guide](https://milvus.io/docs/reranking.md).
-
 ## Documentation
 See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.
 
@@ -198,16 +111,13 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `uri` | `<class 'str'>` | No |  | The URI of the Milvus server |
+| `uri` | `<class 'str'>` | No | PydanticUndefined | The URI of the Milvus server |
-| `token` | `str \| None` | No |  | The token of the Milvus server |
+| `token` | `str \| None` | No | PydanticUndefined | The token of the Milvus server |
 | `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
 | `config` | `dict` | No | {} | This configuration allows additional fields to be passed through to the underlying Milvus client. See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. |
 
-```{note}
+> **Note**: This configuration class accepts additional fields beyond those listed above. You can pass any additional configuration options that will be forwarded to the underlying provider.
- This configuration class accepts additional fields beyond those listed above. You can pass any additional configuration options that will be forwarded to the underlying provider.
- ```
-
 
 ## Sample Configuration
 

docs/source/references/llama_cli_reference/download_models.md

@@ -19,7 +19,7 @@ You have two ways to install Llama Stack:
     cd ~/local
     git clone git@github.com:meta-llama/llama-stack.git
 
-    uv venv myenv --python 3.12
+    python -m venv myenv
     source myenv/bin/activate  # On Windows: myenv\Scripts\activate
 
     cd llama-stack
@@ -128,9 +128,7 @@ llama download --source huggingface --model-id Prompt-Guard-86M --ignore-pattern
 
 **Important:** Set your environment variable `HF_TOKEN` or pass in `--hf-token` to the command to validate your access. You can find your token at [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens).
 
-```{tip}
+> **Tip:** Default for `llama download` is to run with `--ignore-patterns *.safetensors` since we use the `.pth` files in the `original` folder. For Llama Guard and Prompt Guard, however, we need safetensors. Hence, please run with `--ignore-patterns original` so that safetensors are downloaded and `.pth` files are ignored.
-Default for `llama download` is to run with `--ignore-patterns *.safetensors` since we use the `.pth` files in the `original` folder. For Llama Guard and Prompt Guard, however, we need safetensors. Hence, please run with `--ignore-patterns original` so that safetensors are downloaded and `.pth` files are ignored.
-```
 
 ## List the downloaded models
 

docs/source/references/llama_cli_reference/index.md

@@ -19,7 +19,7 @@ You have two ways to install Llama Stack:
     cd ~/local
     git clone git@github.com:meta-llama/llama-stack.git
 
-    uv venv myenv --python 3.12
+    python -m venv myenv
     source myenv/bin/activate  # On Windows: myenv\Scripts\activate
 
     cd llama-stack
@@ -152,9 +152,7 @@ llama download --source huggingface --model-id Prompt-Guard-86M --ignore-pattern
 
 **Important:** Set your environment variable `HF_TOKEN` or pass in `--hf-token` to the command to validate your access. You can find your token at [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens).
 
-```{tip}
+> **Tip:** Default for `llama download` is to run with `--ignore-patterns *.safetensors` since we use the `.pth` files in the `original` folder. For Llama Guard and Prompt Guard, however, we need safetensors. Hence, please run with `--ignore-patterns original` so that safetensors are downloaded and `.pth` files are ignored.
-Default for `llama download` is to run with `--ignore-patterns *.safetensors` since we use the `.pth` files in the `original` folder. For Llama Guard and Prompt Guard, however, we need safetensors. Hence, please run with `--ignore-patterns original` so that safetensors are downloaded and `.pth` files are ignored.
-```
 
 ## List the downloaded models
 

llama_stack/apis/agents/agents.py

@@ -706,7 +706,6 @@ class Agents(Protocol):
         temperature: float | None = None,
         text: OpenAIResponseText | None = None,
         tools: list[OpenAIResponseInputTool] | None = None,
-        include: list[str] | None = None,
         max_infer_iters: int | None = 10,  # this is an extension to the OpenAI API
     ) -> OpenAIResponseObject | AsyncIterator[OpenAIResponseObjectStream]:
         """Create a new OpenAI response.
@@ -714,7 +713,6 @@ class Agents(Protocol):
         :param input: Input message(s) to create the response.
         :param model: The underlying LLM used for completions.
         :param previous_response_id: (Optional) if specified, the new response will be a continuation of the previous response. This can be used to easily fork-off new responses from existing responses.
-        :param include: (Optional) Additional fields to include in the response.
         :returns: An OpenAIResponseObject.
         """
         ...

llama_stack/apis/agents/openai_responses.py

@@ -170,23 +170,6 @@ class OpenAIResponseOutputMessageWebSearchToolCall(BaseModel):
     type: Literal["web_search_call"] = "web_search_call"
 
 
-class OpenAIResponseOutputMessageFileSearchToolCallResults(BaseModel):
-    """Search results returned by the file search operation.
-
-    :param attributes: (Optional) Key-value attributes associated with the file
-    :param file_id: Unique identifier of the file containing the result
-    :param filename: Name of the file containing the result
-    :param score: Relevance score for this search result (between 0 and 1)
-    :param text: Text content of the search result
-    """
-
-    attributes: dict[str, Any]
-    file_id: str
-    filename: str
-    score: float
-    text: str
-
-
 @json_schema_type
 class OpenAIResponseOutputMessageFileSearchToolCall(BaseModel):
     """File search tool call output message for OpenAI responses.
@@ -202,7 +185,7 @@ class OpenAIResponseOutputMessageFileSearchToolCall(BaseModel):
     queries: list[str]
     status: str
     type: Literal["file_search_call"] = "file_search_call"
-    results: list[OpenAIResponseOutputMessageFileSearchToolCallResults] | None = None
+    results: list[dict[str, Any]] | None = None
 
 
 @json_schema_type
@@ -623,62 +606,6 @@ class OpenAIResponseObjectStreamResponseMcpCallCompleted(BaseModel):
     type: Literal["response.mcp_call.completed"] = "response.mcp_call.completed"
 
 
-@json_schema_type
-class OpenAIResponseContentPartOutputText(BaseModel):
-    type: Literal["output_text"] = "output_text"
-    text: str
-    # TODO: add annotations, logprobs, etc.
-
-
-@json_schema_type
-class OpenAIResponseContentPartRefusal(BaseModel):
-    type: Literal["refusal"] = "refusal"
-    refusal: str
-
-
-OpenAIResponseContentPart = Annotated[
-    OpenAIResponseContentPartOutputText | OpenAIResponseContentPartRefusal,
-    Field(discriminator="type"),
-]
-register_schema(OpenAIResponseContentPart, name="OpenAIResponseContentPart")
-
-
-@json_schema_type
-class OpenAIResponseObjectStreamResponseContentPartAdded(BaseModel):
-    """Streaming event for when a new content part is added to a response item.
-
-    :param response_id: Unique identifier of the response containing this content
-    :param item_id: Unique identifier of the output item containing this content part
-    :param part: The content part that was added
-    :param sequence_number: Sequential number for ordering streaming events
-    :param type: Event type identifier, always "response.content_part.added"
-    """
-
-    response_id: str
-    item_id: str
-    part: OpenAIResponseContentPart
-    sequence_number: int
-    type: Literal["response.content_part.added"] = "response.content_part.added"
-
-
-@json_schema_type
-class OpenAIResponseObjectStreamResponseContentPartDone(BaseModel):
-    """Streaming event for when a content part is completed.
-
-    :param response_id: Unique identifier of the response containing this content
-    :param item_id: Unique identifier of the output item containing this content part
-    :param part: The completed content part
-    :param sequence_number: Sequential number for ordering streaming events
-    :param type: Event type identifier, always "response.content_part.done"
-    """
-
-    response_id: str
-    item_id: str
-    part: OpenAIResponseContentPart
-    sequence_number: int
-    type: Literal["response.content_part.done"] = "response.content_part.done"
-
-
 OpenAIResponseObjectStream = Annotated[
     OpenAIResponseObjectStreamResponseCreated
     | OpenAIResponseObjectStreamResponseOutputItemAdded
@@ -698,8 +625,6 @@ OpenAIResponseObjectStream = Annotated[
     | OpenAIResponseObjectStreamResponseMcpCallInProgress
     | OpenAIResponseObjectStreamResponseMcpCallFailed
     | OpenAIResponseObjectStreamResponseMcpCallCompleted
-    | OpenAIResponseObjectStreamResponseContentPartAdded
-    | OpenAIResponseObjectStreamResponseContentPartDone
     | OpenAIResponseObjectStreamResponseCompleted,
     Field(discriminator="type"),
 ]

llama_stack/apis/batches/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-from .batches import Batches, BatchObject, ListBatchesResponse
-
-__all__ = ["Batches", "BatchObject", "ListBatchesResponse"]

llama_stack/apis/batches/batches.py

@@ -1,89 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-from typing import Literal, Protocol, runtime_checkable
-
-from pydantic import BaseModel, Field
-
-from llama_stack.schema_utils import json_schema_type, webmethod
-
-try:
-    from openai.types import Batch as BatchObject
-except ImportError as e:
-    raise ImportError("OpenAI package is required for batches API. Please install it with: pip install openai") from e
-
-
-@json_schema_type
-class ListBatchesResponse(BaseModel):
-    """Response containing a list of batch objects."""
-
-    object: Literal["list"] = "list"
-    data: list[BatchObject] = Field(..., description="List of batch objects")
-    first_id: str | None = Field(default=None, description="ID of the first batch in the list")
-    last_id: str | None = Field(default=None, description="ID of the last batch in the list")
-    has_more: bool = Field(default=False, description="Whether there are more batches available")
-
-
-@runtime_checkable
-class Batches(Protocol):
-    """Protocol for batch processing API operations.
-
-    The Batches API enables efficient processing of multiple requests in a single operation,
-    particularly useful for processing large datasets, batch evaluation workflows, and
-    cost-effective inference at scale.
-
-    Note: This API is currently under active development and may undergo changes.
-    """
-
-    @webmethod(route="/openai/v1/batches", method="POST")
-    async def create_batch(
-        self,
-        input_file_id: str,
-        endpoint: str,
-        completion_window: Literal["24h"],
-        metadata: dict[str, str] | None = None,
-    ) -> BatchObject:
-        """Create a new batch for processing multiple API requests.
-
-        :param input_file_id: The ID of an uploaded file containing requests for the batch.
-        :param endpoint: The endpoint to be used for all requests in the batch.
-        :param completion_window: The time window within which the batch should be processed.
-        :param metadata: Optional metadata for the batch.
-        :returns: The created batch object.
-        """
-        ...
-
-    @webmethod(route="/openai/v1/batches/{batch_id}", method="GET")
-    async def retrieve_batch(self, batch_id: str) -> BatchObject:
-        """Retrieve information about a specific batch.
-
-        :param batch_id: The ID of the batch to retrieve.
-        :returns: The batch object.
-        """
-        ...
-
-    @webmethod(route="/openai/v1/batches/{batch_id}/cancel", method="POST")
-    async def cancel_batch(self, batch_id: str) -> BatchObject:
-        """Cancel a batch that is in progress.
-
-        :param batch_id: The ID of the batch to cancel.
-        :returns: The updated batch object.
-        """
-        ...
-
-    @webmethod(route="/openai/v1/batches", method="GET")
-    async def list_batches(
-        self,
-        after: str | None = None,
-        limit: int = 20,
-    ) -> ListBatchesResponse:
-        """List all batches for the current user.
-
-        :param after: A cursor for pagination; returns batches after this batch ID.
-        :param limit: Number of batches to return (default 20, max 100).
-        :returns: A list of batch objects.
-        """
-        ...

llama_stack/apis/common/errors.py

@@ -10,16 +10,6 @@
 #   3. All classes should propogate the inherited __init__ function otherwise via 'super().__init__(message)'
 
 
-class ResourceNotFoundError(ValueError):
-    """generic exception for a missing Llama Stack resource"""
-
-    def __init__(self, resource_name: str, resource_type: str, client_list: str) -> None:
-        message = (
-            f"{resource_type} '{resource_name}' not found. Use '{client_list}' to list available {resource_type}s."
-        )
-        super().__init__(message)
-
-
 class UnsupportedModelError(ValueError):
     """raised when model is not present in the list of supported models"""
 
@@ -28,32 +18,38 @@ class UnsupportedModelError(ValueError):
         super().__init__(message)
 
 
-class ModelNotFoundError(ResourceNotFoundError):
+class ModelNotFoundError(ValueError):
     """raised when Llama Stack cannot find a referenced model"""
 
     def __init__(self, model_name: str) -> None:
-        super().__init__(model_name, "Model", "client.models.list()")
+        message = f"Model '{model_name}' not found. Use client.models.list() to list available models."
+        super().__init__(message)
 
 
-class VectorStoreNotFoundError(ResourceNotFoundError):
+class VectorStoreNotFoundError(ValueError):
     """raised when Llama Stack cannot find a referenced vector store"""
 
     def __init__(self, vector_store_name: str) -> None:
-        super().__init__(vector_store_name, "Vector Store", "client.vector_dbs.list()")
+        message = f"Vector store '{vector_store_name}' not found. Use client.vector_dbs.list() to list available vector stores."
+        super().__init__(message)
 
 
-class DatasetNotFoundError(ResourceNotFoundError):
+class DatasetNotFoundError(ValueError):
     """raised when Llama Stack cannot find a referenced dataset"""
 
     def __init__(self, dataset_name: str) -> None:
-        super().__init__(dataset_name, "Dataset", "client.datasets.list()")
+        message = f"Dataset '{dataset_name}' not found. Use client.datasets.list() to list available datasets."
+        super().__init__(message)
 
 
-class ToolGroupNotFoundError(ResourceNotFoundError):
+class ToolGroupNotFoundError(ValueError):
     """raised when Llama Stack cannot find a referenced tool group"""
 
     def __init__(self, toolgroup_name: str) -> None:
-        super().__init__(toolgroup_name, "Tool Group", "client.toolgroups.list()")
+        message = (
+            f"Tool group '{toolgroup_name}' not found. Use client.toolgroups.list() to list available tool groups."
+        )
+        super().__init__(message)
 
 
 class SessionNotFoundError(ValueError):
@@ -62,20 +58,3 @@ class SessionNotFoundError(ValueError):
     def __init__(self, session_name: str) -> None:
         message = f"Session '{session_name}' not found or access denied."
         super().__init__(message)
-
-
-class ModelTypeError(TypeError):
-    """raised when a model is present but not the correct type"""
-
-    def __init__(self, model_name: str, model_type: str, expected_model_type: str) -> None:
-        message = (
-            f"Model '{model_name}' is of type '{model_type}' rather than the expected type '{expected_model_type}'"
-        )
-        super().__init__(message)
-
-
-class ConflictError(ValueError):
-    """raised when an operation cannot be performed due to a conflict with the current state"""
-
-    def __init__(self, message: str) -> None:
-        super().__init__(message)

llama_stack/apis/datatypes.py

@@ -86,7 +86,6 @@ class Api(Enum, metaclass=DynamicApiMeta):
     :cvar inference: Text generation, chat completions, and embeddings
     :cvar safety: Content moderation and safety shields
     :cvar agents: Agent orchestration and execution
-    :cvar batches: Batch processing for asynchronous API requests
     :cvar vector_io: Vector database operations and queries
     :cvar datasetio: Dataset input/output operations
     :cvar scoring: Model output evaluation and scoring
@@ -109,7 +108,6 @@ class Api(Enum, metaclass=DynamicApiMeta):
     inference = "inference"
     safety = "safety"
     agents = "agents"
-    batches = "batches"
     vector_io = "vector_io"
     datasetio = "datasetio"
     scoring = "scoring"

llama_stack/apis/files/files.py

@@ -22,7 +22,6 @@ class OpenAIFilePurpose(StrEnum):
     """
 
     ASSISTANTS = "assistants"
-    BATCH = "batch"
     # TODO: Add other purposes as needed
 
 

llama_stack/apis/safety/safety.py

@@ -15,36 +15,6 @@ from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
 from llama_stack.schema_utils import json_schema_type, webmethod
 
 
-@json_schema_type
-class ModerationObjectResults(BaseModel):
-    """A moderation object.
-    :param flagged: Whether any of the below categories are flagged.
-    :param categories: A list of the categories, and whether they are flagged or not.
-    :param category_applied_input_types: A list of the categories along with the input type(s) that the score applies to.
-    :param category_scores: A list of the categories along with their scores as predicted by model.
-    """
-
-    flagged: bool
-    categories: dict[str, bool] | None = None
-    category_applied_input_types: dict[str, list[str]] | None = None
-    category_scores: dict[str, float] | None = None
-    user_message: str | None = None
-    metadata: dict[str, Any] = Field(default_factory=dict)
-
-
-@json_schema_type
-class ModerationObject(BaseModel):
-    """A moderation object.
-    :param id: The unique identifier for the moderation request.
-    :param model: The model used to generate the moderation results.
-    :param results: A list of moderation objects
-    """
-
-    id: str
-    model: str
-    results: list[ModerationObjectResults]
-
-
 @json_schema_type
 class ViolationLevel(Enum):
     """Severity level of a safety violation.
@@ -112,13 +82,3 @@ class Safety(Protocol):
         :returns: A RunShieldResponse.
         """
         ...
-
-    @webmethod(route="/openai/v1/moderations", method="POST")
-    async def run_moderation(self, input: str | list[str], model: str) -> ModerationObject:
-        """Classifies if text and/or image inputs are potentially harmful.
-        :param input: Input (or inputs) to classify.
-        Can be a single string, an array of strings, or an array of multi-modal input objects similar to other models.
-        :param model: The content moderation model you would like to use.
-        :returns: A moderation object.
-        """
-        ...

llama_stack/apis/shields/shields.py

@@ -83,11 +83,3 @@ class Shields(Protocol):
         :returns: A Shield.
         """
         ...
-
-    @webmethod(route="/shields/{identifier:path}", method="DELETE")
-    async def unregister_shield(self, identifier: str) -> None:
-        """Unregister a shield.
-
-        :param identifier: The identifier of the shield to unregister.
-        """
-        ...

llama_stack/core/build.py

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 import importlib.resources
+import logging
 import sys
 
 from pydantic import BaseModel
@@ -16,10 +17,9 @@ from llama_stack.core.external import load_external_apis
 from llama_stack.core.utils.exec import run_command
 from llama_stack.core.utils.image_types import LlamaStackImageType
 from llama_stack.distributions.template import DistributionTemplate
-from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api
 
-log = get_logger(name=__name__, category="core")
+log = logging.getLogger(__name__)
 
 # These are the dependencies needed by the distribution server.
 # `llama-stack` is automatically installed by the installation script.
@@ -91,7 +91,7 @@ def get_provider_dependencies(
 
 
 def print_pip_install_help(config: BuildConfig):
-    normal_deps, special_deps, _ = get_provider_dependencies(config)
+    normal_deps, special_deps = get_provider_dependencies(config)
 
     cprint(
         f"Please install needed dependencies using the following commands:\n\nuv pip install {' '.join(normal_deps)}",

llama_stack/core/build_conda_env.sh

@@ -0,0 +1,207 @@
+#!/bin/bash
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+LLAMA_STACK_DIR=${LLAMA_STACK_DIR:-}
+LLAMA_STACK_CLIENT_DIR=${LLAMA_STACK_CLIENT_DIR:-}
+TEST_PYPI_VERSION=${TEST_PYPI_VERSION:-}
+PYPI_VERSION=${PYPI_VERSION:-}
+# This timeout (in seconds) is necessary when installing PyTorch via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+UV_HTTP_TIMEOUT=${UV_HTTP_TIMEOUT:-500}
+
+set -euo pipefail
+
+# Define color codes
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+NC='\033[0m' # No Color
+
+SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
+source "$SCRIPT_DIR/common.sh"
+
+# Usage function
+usage() {
+  echo "Usage: $0 --env-name <conda_env_name> --build-file-path <build_file_path> --normal-deps <pip_dependencies> [--external-provider-deps <external_provider_deps>] [--optional-deps <special_pip_deps>]"
+  echo "Example: $0 --env-name my-conda-env --build-file-path ./my-stack-build.yaml --normal-deps 'numpy pandas scipy' --external-provider-deps 'foo' --optional-deps 'bar'"
+  exit 1
+}
+
+# Parse arguments
+env_name=""
+build_file_path=""
+normal_deps=""
+external_provider_deps=""
+optional_deps=""
+
+while [[ $# -gt 0 ]]; do
+  key="$1"
+  case "$key" in
+    --env-name)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --env-name requires a string value" >&2
+        usage
+      fi
+      env_name="$2"
+      shift 2
+      ;;
+    --build-file-path)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --build-file-path requires a string value" >&2
+        usage
+      fi
+      build_file_path="$2"
+      shift 2
+      ;;
+    --normal-deps)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --normal-deps requires a string value" >&2
+        usage
+      fi
+      normal_deps="$2"
+      shift 2
+      ;;
+    --external-provider-deps)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --external-provider-deps requires a string value" >&2
+        usage
+      fi
+      external_provider_deps="$2"
+      shift 2
+      ;;
+    --optional-deps)
+      if [[ -z "$2" || "$2" == --* ]]; then
+        echo "Error: --optional-deps requires a string value" >&2
+        usage
+      fi
+      optional_deps="$2"
+      shift 2
+      ;;
+    *)
+      echo "Unknown option: $1" >&2
+      usage
+      ;;
+  esac
+done
+
+# Check required arguments
+if [[ -z "$env_name" || -z "$build_file_path" || -z "$normal_deps" ]]; then
+  echo "Error: --env-name, --build-file-path, and --normal-deps are required." >&2
+  usage
+fi
+
+if [ -n "$LLAMA_STACK_DIR" ]; then
+  echo "Using llama-stack-dir=$LLAMA_STACK_DIR"
+fi
+if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
+  echo "Using llama-stack-client-dir=$LLAMA_STACK_CLIENT_DIR"
+fi
+
+ensure_conda_env_python310() {
+  # Use only global variables set by flag parser
+  local python_version="3.12"
+
+  if ! is_command_available conda; then
+    printf "${RED}Error: conda command not found. Is Conda installed and in your PATH?${NC}" >&2
+    exit 1
+  fi
+
+  if conda env list | grep -q "^${env_name} "; then
+    printf "Conda environment '${env_name}' exists. Checking Python version...\n"
+    current_version=$(conda run -n "${env_name}" python --version 2>&1 | cut -d' ' -f2 | cut -d'.' -f1,2)
+    if [ "$current_version" = "$python_version" ]; then
+      printf "Environment '${env_name}' already has Python ${python_version}. No action needed.\n"
+    else
+      printf "Updating environment '${env_name}' to Python ${python_version}...\n"
+      conda install -n "${env_name}" python="${python_version}" -y
+    fi
+  else
+    printf "Conda environment '${env_name}' does not exist. Creating with Python ${python_version}...\n"
+    conda create -n "${env_name}" python="${python_version}" -y
+  fi
+
+  eval "$(conda shell.bash hook)"
+  conda deactivate && conda activate "${env_name}"
+  "$CONDA_PREFIX"/bin/pip install uv
+
+  if [ -n "$TEST_PYPI_VERSION" ]; then
+    uv pip install fastapi libcst
+    uv pip install --extra-index-url https://test.pypi.org/simple/ \
+      llama-stack=="$TEST_PYPI_VERSION" \
+      "$normal_deps"
+    if [ -n "$optional_deps" ]; then
+      IFS='#' read -ra parts <<<"$optional_deps"
+      for part in "${parts[@]}"; do
+        echo "$part"
+        uv pip install $part
+      done
+    fi
+    if [ -n "$external_provider_deps" ]; then
+      IFS='#' read -ra parts <<<"$external_provider_deps"
+      for part in "${parts[@]}"; do
+        echo "$part"
+        uv pip install "$part"
+      done
+    fi
+  else
+    if [ -n "$LLAMA_STACK_DIR" ]; then
+      if [ ! -d "$LLAMA_STACK_DIR" ]; then
+        printf "${RED}Warning: LLAMA_STACK_DIR is set but directory does not exist: $LLAMA_STACK_DIR${NC}\n" >&2
+        exit 1
+      fi
+      printf "Installing from LLAMA_STACK_DIR: $LLAMA_STACK_DIR\n"
+      uv pip install --no-cache-dir -e "$LLAMA_STACK_DIR"
+    else
+      PYPI_VERSION="${PYPI_VERSION:-}"
+      if [ -n "$PYPI_VERSION" ]; then
+        SPEC_VERSION="llama-stack==${PYPI_VERSION}"
+      else
+        SPEC_VERSION="llama-stack"
+      fi
+      uv pip install --no-cache-dir "$SPEC_VERSION"
+    fi
+    if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
+      if [ ! -d "$LLAMA_STACK_CLIENT_DIR" ]; then
+        printf "${RED}Warning: LLAMA_STACK_CLIENT_DIR is set but directory does not exist: $LLAMA_STACK_CLIENT_DIR${NC}\n" >&2
+        exit 1
+      fi
+      printf "Installing from LLAMA_STACK_CLIENT_DIR: $LLAMA_STACK_CLIENT_DIR\n"
+      uv pip install --no-cache-dir -e "$LLAMA_STACK_CLIENT_DIR"
+    fi
+    printf "Installing pip dependencies\n"
+    uv pip install $normal_deps
+    if [ -n "$optional_deps" ]; then
+      IFS='#' read -ra parts <<<"$optional_deps"
+      for part in "${parts[@]}"; do
+        echo "$part"
+        uv pip install $part
+      done
+    fi
+    if [ -n "$external_provider_deps" ]; then
+      IFS='#' read -ra parts <<<"$external_provider_deps"
+      for part in "${parts[@]}"; do
+        echo "Getting provider spec for module: $part and installing dependencies"
+        package_name=$(echo "$part" | sed 's/[<>=!].*//')
+        python3 -c "
+import importlib
+import sys
+try:
+    module = importlib.import_module(f'$package_name.provider')
+    spec = module.get_provider_spec()
+    if hasattr(spec, 'pip_packages') and spec.pip_packages:
+        print('\\n'.join(spec.pip_packages))
+except Exception as e:
+    print(f'Error getting provider spec for $package_name: {e}', file=sys.stderr)
+" | uv pip install -r -
+      done
+    fi
+  fi
+  mv "$build_file_path" "$CONDA_PREFIX"/llamastack-build.yaml
+  echo "Build spec configuration saved at $CONDA_PREFIX/llamastack-build.yaml"
+}
+
+ensure_conda_env_python310 "$env_name" "$build_file_path" "$normal_deps" "$optional_deps" "$external_provider_deps"

llama_stack/core/build_venv.sh

@@ -151,37 +151,23 @@ run() {
     fi
   else
     if [ -n "$LLAMA_STACK_DIR" ]; then
-      # only warn if DIR does not start with "git+"
+      if [ ! -d "$LLAMA_STACK_DIR" ]; then
-      if [ ! -d "$LLAMA_STACK_DIR" ] && [[ "$LLAMA_STACK_DIR" != git+* ]]; then
         printf "${RED}Warning: LLAMA_STACK_DIR is set but directory does not exist: %s${NC}\n" "$LLAMA_STACK_DIR" >&2
         exit 1
       fi
       printf "Installing from LLAMA_STACK_DIR: %s\n"  "$LLAMA_STACK_DIR"
-      # editable only if LLAMA_STACK_DIR does not start with "git+"
+      uv pip install --no-cache-dir -e "$LLAMA_STACK_DIR"
-      if [[ "$LLAMA_STACK_DIR" != git+* ]]; then
-        EDITABLE="-e"
-      else
-        EDITABLE=""
-      fi
-      uv pip install --no-cache-dir $EDITABLE "$LLAMA_STACK_DIR"
     else
       uv pip install --no-cache-dir llama-stack
     fi
 
     if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
-      # only warn if DIR does not start with "git+"
+      if [ ! -d "$LLAMA_STACK_CLIENT_DIR" ]; then
-      if [ ! -d "$LLAMA_STACK_CLIENT_DIR" ] && [[ "$LLAMA_STACK_CLIENT_DIR" != git+* ]]; then
         printf "${RED}Warning: LLAMA_STACK_CLIENT_DIR is set but directory does not exist: %s${NC}\n" "$LLAMA_STACK_CLIENT_DIR" >&2
         exit 1
       fi
       printf "Installing from LLAMA_STACK_CLIENT_DIR: %s\n" "$LLAMA_STACK_CLIENT_DIR"
-      # editable only if LLAMA_STACK_CLIENT_DIR does not start with "git+"
+      uv pip install --no-cache-dir -e "$LLAMA_STACK_CLIENT_DIR"
-      if [[ "$LLAMA_STACK_CLIENT_DIR" != git+* ]]; then
-        EDITABLE="-e"
-      else
-        EDITABLE=""
-      fi
-      uv pip install --no-cache-dir $EDITABLE "$LLAMA_STACK_CLIENT_DIR"
     fi
 
     printf "Installing pip dependencies\n"

llama_stack/core/configure.py

@@ -3,6 +3,7 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+import logging
 import textwrap
 from typing import Any
 
@@ -20,10 +21,9 @@ from llama_stack.core.stack import cast_image_name_to_string, replace_env_vars
 from llama_stack.core.utils.config_dirs import EXTERNAL_PROVIDERS_DIR
 from llama_stack.core.utils.dynamic import instantiate_class_type
 from llama_stack.core.utils.prompt_for_config import prompt_for_config
-from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api, ProviderSpec
 
-logger = get_logger(name=__name__, category="core")
+logger = logging.getLogger(__name__)
 
 
 def configure_single_provider(registry: dict[str, ProviderSpec], provider: Provider) -> Provider:

llama_stack/core/library_client.py

@@ -7,7 +7,7 @@
 import asyncio
 import inspect
 import json
-import logging  # allow-direct-logging
+import logging
 import os
 import sys
 from concurrent.futures import ThreadPoolExecutor
@@ -48,7 +48,6 @@ from llama_stack.core.stack import (
 from llama_stack.core.utils.config import redact_sensitive_fields
 from llama_stack.core.utils.context import preserve_contexts_async_generator
 from llama_stack.core.utils.exec import in_notebook
-from llama_stack.log import get_logger
 from llama_stack.providers.utils.telemetry.tracing import (
     CURRENT_TRACE_CONTEXT,
     end_trace,
@@ -56,7 +55,7 @@ from llama_stack.providers.utils.telemetry.tracing import (
     start_trace,
 )
 
-logger = get_logger(name=__name__, category="core")
+logger = logging.getLogger(__name__)
 
 T = TypeVar("T")
 
@@ -381,17 +380,8 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         json_content = json.dumps(convert_pydantic_to_json_value(result))
 
         filtered_body = {k: v for k, v in body.items() if not isinstance(v, LibraryClientUploadFile)}
-
-        status_code = httpx.codes.OK
-
-        if options.method.upper() == "DELETE" and result is None:
-            status_code = httpx.codes.NO_CONTENT
-
-        if status_code == httpx.codes.NO_CONTENT:
-            json_content = ""
-
         mock_response = httpx.Response(
-            status_code=status_code,
+            status_code=httpx.codes.OK,
             content=json_content.encode("utf-8"),
             headers={
                 "Content-Type": "application/json",

llama_stack/core/request_headers.py

@@ -6,15 +6,15 @@
 
 import contextvars
 import json
+import logging
 from contextlib import AbstractContextManager
 from typing import Any
 
 from llama_stack.core.datatypes import User
-from llama_stack.log import get_logger
 
 from .utils.dynamic import instantiate_class_type
 
-log = get_logger(name=__name__, category="core")
+log = logging.getLogger(__name__)
 
 # Context variable for request provider data and auth attributes
 PROVIDER_DATA_VAR = contextvars.ContextVar("provider_data", default=None)

llama_stack/core/resolver.py

@@ -8,7 +8,6 @@ import inspect
 from typing import Any
 
 from llama_stack.apis.agents import Agents
-from llama_stack.apis.batches import Batches
 from llama_stack.apis.benchmarks import Benchmarks
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
@@ -76,7 +75,6 @@ def api_protocol_map(external_apis: dict[Api, ExternalApiSpec] | None = None) ->
         Api.agents: Agents,
         Api.inference: Inference,
         Api.inspect: Inspect,
-        Api.batches: Batches,
         Api.vector_io: VectorIO,
         Api.vector_dbs: VectorDBs,
         Api.models: Models,

llama_stack/core/routers/inference.py

@@ -7,7 +7,6 @@
 import asyncio
 import time
 from collections.abc import AsyncGenerator, AsyncIterator
-from datetime import UTC, datetime
 from typing import Annotated, Any
 
 from openai.types.chat import ChatCompletionToolChoiceOptionParam as OpenAIChatCompletionToolChoiceOptionParam
@@ -18,7 +17,7 @@ from llama_stack.apis.common.content_types import (
     InterleavedContent,
     InterleavedContentItem,
 )
-from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError
+from llama_stack.apis.common.errors import ModelNotFoundError
 from llama_stack.apis.inference import (
     BatchChatCompletionResponse,
     BatchCompletionResponse,
@@ -26,21 +25,14 @@ from llama_stack.apis.inference import (
     ChatCompletionResponseEventType,
     ChatCompletionResponseStreamChunk,
     CompletionMessage,
-    CompletionResponse,
-    CompletionResponseStreamChunk,
     EmbeddingsResponse,
     EmbeddingTaskType,
     Inference,
     ListOpenAIChatCompletionResponse,
     LogProbConfig,
     Message,
-    OpenAIAssistantMessageParam,
     OpenAIChatCompletion,
     OpenAIChatCompletionChunk,
-    OpenAIChatCompletionToolCall,
-    OpenAIChatCompletionToolCallFunction,
-    OpenAIChoice,
-    OpenAIChoiceLogprobs,
     OpenAICompletion,
     OpenAICompletionWithInputMessages,
     OpenAIEmbeddingsResponse,
@@ -63,9 +55,10 @@ from llama_stack.models.llama.llama3.chat_format import ChatFormat
 from llama_stack.models.llama.llama3.tokenizer import Tokenizer
 from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
 from llama_stack.providers.utils.inference.inference_store import InferenceStore
+from llama_stack.providers.utils.inference.stream_utils import stream_and_store_openai_completion
 from llama_stack.providers.utils.telemetry.tracing import get_current_span
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="core")
 
 
 class InferenceRouter(Inference):
@@ -126,7 +119,6 @@ class InferenceRouter(Inference):
         if span is None:
             logger.warning("No span found for token usage metrics")
             return []
-
         metrics = [
             ("prompt_tokens", prompt_tokens),
             ("completion_tokens", completion_tokens),
@@ -140,7 +132,7 @@ class InferenceRouter(Inference):
                     span_id=span.span_id,
                     metric=metric_name,
                     value=value,
-                    timestamp=datetime.now(UTC),
+                    timestamp=time.time(),
                     unit="tokens",
                     attributes={
                         "model_id": model.model_id,
@@ -177,15 +169,6 @@ class InferenceRouter(Inference):
             encoded = self.formatter.encode_content(messages)
         return len(encoded.tokens) if encoded and encoded.tokens else 0
 
-    async def _get_model(self, model_id: str, expected_model_type: str) -> Model:
-        """takes a model id and gets model after ensuring that it is accessible and of the correct type"""
-        model = await self.routing_table.get_model(model_id)
-        if model is None:
-            raise ModelNotFoundError(model_id)
-        if model.model_type != expected_model_type:
-            raise ModelTypeError(model_id, model.model_type, expected_model_type)
-        return model
-
     async def chat_completion(
         self,
         model_id: str,
@@ -204,7 +187,11 @@ class InferenceRouter(Inference):
         )
         if sampling_params is None:
             sampling_params = SamplingParams()
-        model = await self._get_model(model_id, ModelType.llm)
+        model = await self.routing_table.get_model(model_id)
+        if model is None:
+            raise ModelNotFoundError(model_id)
+        if model.model_type == ModelType.embedding:
+            raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
         if tool_config:
             if tool_choice and tool_choice != tool_config.tool_choice:
                 raise ValueError("tool_choice and tool_config.tool_choice must match")
@@ -247,26 +234,49 @@ class InferenceRouter(Inference):
         prompt_tokens = await self._count_tokens(messages, tool_config.tool_prompt_format)
 
         if stream:
-            response_stream = await provider.chat_completion(**params)
-            return self.stream_tokens_and_compute_metrics(
-                response=response_stream,
-                prompt_tokens=prompt_tokens,
-                model=model,
-                tool_prompt_format=tool_config.tool_prompt_format,
-            )
 
-        response = await provider.chat_completion(**params)
+            async def stream_generator():
-        metrics = await self.count_tokens_and_compute_metrics(
+                completion_text = ""
-            response=response,
+                async for chunk in await provider.chat_completion(**params):
-            prompt_tokens=prompt_tokens,
+                    if chunk.event.event_type == ChatCompletionResponseEventType.progress:
-            model=model,
+                        if chunk.event.delta.type == "text":
-            tool_prompt_format=tool_config.tool_prompt_format,
+                            completion_text += chunk.event.delta.text
-        )
+                    if chunk.event.event_type == ChatCompletionResponseEventType.complete:
-        # these metrics will show up in the client response.
+                        completion_tokens = await self._count_tokens(
-        response.metrics = (
+                            [
-            metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics
+                                CompletionMessage(
-        )
+                                    content=completion_text,
-        return response
+                                    stop_reason=StopReason.end_of_turn,
+                                )
+                            ],
+                            tool_config.tool_prompt_format,
+                        )
+                        total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
+                        metrics = await self._compute_and_log_token_usage(
+                            prompt_tokens or 0,
+                            completion_tokens or 0,
+                            total_tokens,
+                            model,
+                        )
+                        chunk.metrics = metrics if chunk.metrics is None else chunk.metrics + metrics
+                    yield chunk
+
+            return stream_generator()
+        else:
+            response = await provider.chat_completion(**params)
+            completion_tokens = await self._count_tokens(
+                [response.completion_message],
+                tool_config.tool_prompt_format,
+            )
+            total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
+            metrics = await self._compute_and_log_token_usage(
+                prompt_tokens or 0,
+                completion_tokens or 0,
+                total_tokens,
+                model,
+            )
+            response.metrics = metrics if response.metrics is None else response.metrics + metrics
+            return response
 
     async def batch_chat_completion(
         self,
@@ -306,7 +316,11 @@ class InferenceRouter(Inference):
         logger.debug(
             f"InferenceRouter.completion: {model_id=}, {stream=}, {content=}, {sampling_params=}, {response_format=}",
         )
-        model = await self._get_model(model_id, ModelType.llm)
+        model = await self.routing_table.get_model(model_id)
+        if model is None:
+            raise ModelNotFoundError(model_id)
+        if model.model_type == ModelType.embedding:
+            raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
         provider = await self.routing_table.get_provider_impl(model_id)
         params = dict(
             model_id=model_id,
@@ -318,20 +332,39 @@ class InferenceRouter(Inference):
         )
 
         prompt_tokens = await self._count_tokens(content)
-        response = await provider.completion(**params)
+
         if stream:
-            return self.stream_tokens_and_compute_metrics(
+
-                response=response,
+            async def stream_generator():
-                prompt_tokens=prompt_tokens,
+                completion_text = ""
-                model=model,
+                async for chunk in await provider.completion(**params):
+                    if hasattr(chunk, "delta"):
+                        completion_text += chunk.delta
+                    if hasattr(chunk, "stop_reason") and chunk.stop_reason and self.telemetry:
+                        completion_tokens = await self._count_tokens(completion_text)
+                        total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
+                        metrics = await self._compute_and_log_token_usage(
+                            prompt_tokens or 0,
+                            completion_tokens or 0,
+                            total_tokens,
+                            model,
+                        )
+                        chunk.metrics = metrics if chunk.metrics is None else chunk.metrics + metrics
+                    yield chunk
+
+            return stream_generator()
+        else:
+            response = await provider.completion(**params)
+            completion_tokens = await self._count_tokens(response.content)
+            total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
+            metrics = await self._compute_and_log_token_usage(
+                prompt_tokens or 0,
+                completion_tokens or 0,
+                total_tokens,
+                model,
             )
-
+            response.metrics = metrics if response.metrics is None else response.metrics + metrics
-        metrics = await self.count_tokens_and_compute_metrics(
+            return response
-            response=response, prompt_tokens=prompt_tokens, model=model
-        )
-        response.metrics = metrics if response.metrics is None else response.metrics + metrics
-
-        return response
 
     async def batch_completion(
         self,
@@ -356,7 +389,11 @@ class InferenceRouter(Inference):
         task_type: EmbeddingTaskType | None = None,
     ) -> EmbeddingsResponse:
         logger.debug(f"InferenceRouter.embeddings: {model_id}")
-        await self._get_model(model_id, ModelType.embedding)
+        model = await self.routing_table.get_model(model_id)
+        if model is None:
+            raise ModelNotFoundError(model_id)
+        if model.model_type == ModelType.llm:
+            raise ValueError(f"Model '{model_id}' is an LLM model and does not support embeddings")
         provider = await self.routing_table.get_provider_impl(model_id)
         return await provider.embeddings(
             model_id=model_id,
@@ -392,7 +429,12 @@ class InferenceRouter(Inference):
         logger.debug(
             f"InferenceRouter.openai_completion: {model=}, {stream=}, {prompt=}",
         )
-        model_obj = await self._get_model(model, ModelType.llm)
+        model_obj = await self.routing_table.get_model(model)
+        if model_obj is None:
+            raise ModelNotFoundError(model)
+        if model_obj.model_type == ModelType.embedding:
+            raise ValueError(f"Model '{model}' is an embedding model and does not support completions")
+
         params = dict(
             model=model_obj.identifier,
             prompt=prompt,
@@ -415,29 +457,9 @@ class InferenceRouter(Inference):
             prompt_logprobs=prompt_logprobs,
             suffix=suffix,
         )
+
         provider = await self.routing_table.get_provider_impl(model_obj.identifier)
-        if stream:
+        return await provider.openai_completion(**params)
-            return await provider.openai_completion(**params)
-            # TODO: Metrics do NOT work with openai_completion stream=True due to the fact
-            # that we do not return an AsyncIterator, our tests expect a stream of chunks we cannot intercept currently.
-            # response_stream = await provider.openai_completion(**params)
-
-        response = await provider.openai_completion(**params)
-        if self.telemetry:
-            metrics = self._construct_metrics(
-                prompt_tokens=response.usage.prompt_tokens,
-                completion_tokens=response.usage.completion_tokens,
-                total_tokens=response.usage.total_tokens,
-                model=model_obj,
-            )
-            for metric in metrics:
-                await self.telemetry.log_event(metric)
-
-            # these metrics will show up in the client response.
-            response.metrics = (
-                metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics
-            )
-        return response
 
     async def openai_chat_completion(
         self,
@@ -468,7 +490,11 @@ class InferenceRouter(Inference):
         logger.debug(
             f"InferenceRouter.openai_chat_completion: {model=}, {stream=}, {messages=}",
         )
-        model_obj = await self._get_model(model, ModelType.llm)
+        model_obj = await self.routing_table.get_model(model)
+        if model_obj is None:
+            raise ModelNotFoundError(model)
+        if model_obj.model_type == ModelType.embedding:
+            raise ValueError(f"Model '{model}' is an embedding model and does not support chat completions")
 
         # Use the OpenAI client for a bit of extra input validation without
         # exposing the OpenAI client itself as part of our API surface
@@ -511,38 +537,18 @@ class InferenceRouter(Inference):
             top_p=top_p,
             user=user,
         )
+
         provider = await self.routing_table.get_provider_impl(model_obj.identifier)
         if stream:
             response_stream = await provider.openai_chat_completion(**params)
-
+            if self.store:
-            # For streaming, the provider returns AsyncIterator[OpenAIChatCompletionChunk]
+                return stream_and_store_openai_completion(response_stream, model, self.store, messages)
-            # We need to add metrics to each chunk and store the final completion
+            return response_stream
-            return self.stream_tokens_and_compute_metrics_openai_chat(
+        else:
-                response=response_stream,
+            response = await self._nonstream_openai_chat_completion(provider, params)
-                model=model_obj,
+            if self.store:
-                messages=messages,
+                await self.store.store_chat_completion(response, messages)
-            )
+            return response
-
-        response = await self._nonstream_openai_chat_completion(provider, params)
-
-        # Store the response with the ID that will be returned to the client
-        if self.store:
-            await self.store.store_chat_completion(response, messages)
-
-        if self.telemetry:
-            metrics = self._construct_metrics(
-                prompt_tokens=response.usage.prompt_tokens,
-                completion_tokens=response.usage.completion_tokens,
-                total_tokens=response.usage.total_tokens,
-                model=model_obj,
-            )
-            for metric in metrics:
-                await self.telemetry.log_event(metric)
-            # these metrics will show up in the client response.
-            response.metrics = (
-                metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics
-            )
-        return response
 
     async def openai_embeddings(
         self,
@@ -555,7 +561,12 @@ class InferenceRouter(Inference):
         logger.debug(
             f"InferenceRouter.openai_embeddings: {model=}, input_type={type(input)}, {encoding_format=}, {dimensions=}",
         )
-        model_obj = await self._get_model(model, ModelType.embedding)
+        model_obj = await self.routing_table.get_model(model)
+        if model_obj is None:
+            raise ModelNotFoundError(model)
+        if model_obj.model_type != ModelType.embedding:
+            raise ValueError(f"Model '{model}' is not an embedding model")
+
         params = dict(
             model=model_obj.identifier,
             input=input,
@@ -614,245 +625,3 @@ class InferenceRouter(Inference):
                     status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}"
                 )
         return health_statuses
-
-    async def stream_tokens_and_compute_metrics(
-        self,
-        response,
-        prompt_tokens,
-        model,
-        tool_prompt_format: ToolPromptFormat | None = None,
-    ) -> AsyncGenerator[ChatCompletionResponseStreamChunk, None] | AsyncGenerator[CompletionResponseStreamChunk, None]:
-        completion_text = ""
-        async for chunk in response:
-            complete = False
-            if hasattr(chunk, "event"):  # only ChatCompletions have .event
-                if chunk.event.event_type == ChatCompletionResponseEventType.progress:
-                    if chunk.event.delta.type == "text":
-                        completion_text += chunk.event.delta.text
-                if chunk.event.event_type == ChatCompletionResponseEventType.complete:
-                    complete = True
-                    completion_tokens = await self._count_tokens(
-                        [
-                            CompletionMessage(
-                                content=completion_text,
-                                stop_reason=StopReason.end_of_turn,
-                            )
-                        ],
-                        tool_prompt_format=tool_prompt_format,
-                    )
-            else:
-                if hasattr(chunk, "delta"):
-                    completion_text += chunk.delta
-                if hasattr(chunk, "stop_reason") and chunk.stop_reason and self.telemetry:
-                    complete = True
-                    completion_tokens = await self._count_tokens(completion_text)
-            # if we are done receiving tokens
-            if complete:
-                total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
-
-                # Create a separate span for streaming completion metrics
-                if self.telemetry:
-                    # Log metrics in the new span context
-                    completion_metrics = self._construct_metrics(
-                        prompt_tokens=prompt_tokens,
-                        completion_tokens=completion_tokens,
-                        total_tokens=total_tokens,
-                        model=model,
-                    )
-                    for metric in completion_metrics:
-                        if metric.metric in [
-                            "completion_tokens",
-                            "total_tokens",
-                        ]:  # Only log completion and total tokens
-                            await self.telemetry.log_event(metric)
-
-                        # Return metrics in response
-                        async_metrics = [
-                            MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics
-                        ]
-                        chunk.metrics = async_metrics if chunk.metrics is None else chunk.metrics + async_metrics
-                else:
-                    # Fallback if no telemetry
-                    completion_metrics = self._construct_metrics(
-                        prompt_tokens or 0,
-                        completion_tokens or 0,
-                        total_tokens,
-                        model,
-                    )
-                    async_metrics = [
-                        MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics
-                    ]
-                    chunk.metrics = async_metrics if chunk.metrics is None else chunk.metrics + async_metrics
-            yield chunk
-
-    async def count_tokens_and_compute_metrics(
-        self,
-        response: ChatCompletionResponse | CompletionResponse,
-        prompt_tokens,
-        model,
-        tool_prompt_format: ToolPromptFormat | None = None,
-    ):
-        if isinstance(response, ChatCompletionResponse):
-            content = [response.completion_message]
-        else:
-            content = response.content
-        completion_tokens = await self._count_tokens(messages=content, tool_prompt_format=tool_prompt_format)
-        total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
-
-        # Create a separate span for completion metrics
-        if self.telemetry:
-            # Log metrics in the new span context
-            completion_metrics = self._construct_metrics(
-                prompt_tokens=prompt_tokens,
-                completion_tokens=completion_tokens,
-                total_tokens=total_tokens,
-                model=model,
-            )
-            for metric in completion_metrics:
-                if metric.metric in ["completion_tokens", "total_tokens"]:  # Only log completion and total tokens
-                    await self.telemetry.log_event(metric)
-
-            # Return metrics in response
-            return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics]
-
-        # Fallback if no telemetry
-        metrics = self._construct_metrics(
-            prompt_tokens or 0,
-            completion_tokens or 0,
-            total_tokens,
-            model,
-        )
-        return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics]
-
-    async def stream_tokens_and_compute_metrics_openai_chat(
-        self,
-        response: AsyncIterator[OpenAIChatCompletionChunk],
-        model: Model,
-        messages: list[OpenAIMessageParam] | None = None,
-    ) -> AsyncIterator[OpenAIChatCompletionChunk]:
-        """Stream OpenAI chat completion chunks, compute metrics, and store the final completion."""
-        id = None
-        created = None
-        choices_data: dict[int, dict[str, Any]] = {}
-
-        try:
-            async for chunk in response:
-                # Skip None chunks
-                if chunk is None:
-                    continue
-
-                # Capture ID and created timestamp from first chunk
-                if id is None and chunk.id:
-                    id = chunk.id
-                if created is None and chunk.created:
-                    created = chunk.created
-
-                # Accumulate choice data for final assembly
-                if chunk.choices:
-                    for choice_delta in chunk.choices:
-                        idx = choice_delta.index
-                        if idx not in choices_data:
-                            choices_data[idx] = {
-                                "content_parts": [],
-                                "tool_calls_builder": {},
-                                "finish_reason": None,
-                                "logprobs_content_parts": [],
-                            }
-                        current_choice_data = choices_data[idx]
-
-                        if choice_delta.delta:
-                            delta = choice_delta.delta
-                            if delta.content:
-                                current_choice_data["content_parts"].append(delta.content)
-                            if delta.tool_calls:
-                                for tool_call_delta in delta.tool_calls:
-                                    tc_idx = tool_call_delta.index
-                                    if tc_idx not in current_choice_data["tool_calls_builder"]:
-                                        current_choice_data["tool_calls_builder"][tc_idx] = {
-                                            "id": None,
-                                            "type": "function",
-                                            "function_name_parts": [],
-                                            "function_arguments_parts": [],
-                                        }
-                                    builder = current_choice_data["tool_calls_builder"][tc_idx]
-                                    if tool_call_delta.id:
-                                        builder["id"] = tool_call_delta.id
-                                    if tool_call_delta.type:
-                                        builder["type"] = tool_call_delta.type
-                                    if tool_call_delta.function:
-                                        if tool_call_delta.function.name:
-                                            builder["function_name_parts"].append(tool_call_delta.function.name)
-                                        if tool_call_delta.function.arguments:
-                                            builder["function_arguments_parts"].append(
-                                                tool_call_delta.function.arguments
-                                            )
-                        if choice_delta.finish_reason:
-                            current_choice_data["finish_reason"] = choice_delta.finish_reason
-                        if choice_delta.logprobs and choice_delta.logprobs.content:
-                            current_choice_data["logprobs_content_parts"].extend(choice_delta.logprobs.content)
-
-                # Compute metrics on final chunk
-                if chunk.choices and chunk.choices[0].finish_reason:
-                    completion_text = ""
-                    for choice_data in choices_data.values():
-                        completion_text += "".join(choice_data["content_parts"])
-
-                    # Add metrics to the chunk
-                    if self.telemetry and chunk.usage:
-                        metrics = self._construct_metrics(
-                            prompt_tokens=chunk.usage.prompt_tokens,
-                            completion_tokens=chunk.usage.completion_tokens,
-                            total_tokens=chunk.usage.total_tokens,
-                            model=model,
-                        )
-                        for metric in metrics:
-                            await self.telemetry.log_event(metric)
-
-                yield chunk
-        finally:
-            # Store the final assembled completion
-            if id and self.store and messages:
-                assembled_choices: list[OpenAIChoice] = []
-                for choice_idx, choice_data in choices_data.items():
-                    content_str = "".join(choice_data["content_parts"])
-                    assembled_tool_calls: list[OpenAIChatCompletionToolCall] = []
-                    if choice_data["tool_calls_builder"]:
-                        for tc_build_data in choice_data["tool_calls_builder"].values():
-                            if tc_build_data["id"]:
-                                func_name = "".join(tc_build_data["function_name_parts"])
-                                func_args = "".join(tc_build_data["function_arguments_parts"])
-                                assembled_tool_calls.append(
-                                    OpenAIChatCompletionToolCall(
-                                        id=tc_build_data["id"],
-                                        type=tc_build_data["type"],
-                                        function=OpenAIChatCompletionToolCallFunction(
-                                            name=func_name, arguments=func_args
-                                        ),
-                                    )
-                                )
-                    message = OpenAIAssistantMessageParam(
-                        role="assistant",
-                        content=content_str if content_str else None,
-                        tool_calls=assembled_tool_calls if assembled_tool_calls else None,
-                    )
-                    logprobs_content = choice_data["logprobs_content_parts"]
-                    final_logprobs = OpenAIChoiceLogprobs(content=logprobs_content) if logprobs_content else None
-
-                    assembled_choices.append(
-                        OpenAIChoice(
-                            finish_reason=choice_data["finish_reason"],
-                            index=choice_idx,
-                            message=message,
-                            logprobs=final_logprobs,
-                        )
-                    )
-
-                final_response = OpenAIChatCompletion(
-                    id=id,
-                    choices=assembled_choices,
-                    created=created or int(time.time()),
-                    model=model.identifier,
-                    object="chat.completion",
-                )
-                logger.debug(f"InferenceRouter.completion_response: {final_response}")
-                await self.store.store_chat_completion(final_response, messages)

llama_stack/core/routers/safety.py

@@ -6,9 +6,10 @@
 
 from typing import Any
 
-from llama_stack.apis.inference import Message
+from llama_stack.apis.inference import (
+    Message,
+)
 from llama_stack.apis.safety import RunShieldResponse, Safety
-from llama_stack.apis.safety.safety import ModerationObject
 from llama_stack.apis.shields import Shield
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import RoutingTable
@@ -42,10 +43,6 @@ class SafetyRouter(Safety):
         logger.debug(f"SafetyRouter.register_shield: {shield_id}")
         return await self.routing_table.register_shield(shield_id, provider_shield_id, provider_id, params)
 
-    async def unregister_shield(self, identifier: str) -> None:
-        logger.debug(f"SafetyRouter.unregister_shield: {identifier}")
-        return await self.routing_table.unregister_shield(identifier)
-
     async def run_shield(
         self,
         shield_id: str,
@@ -59,27 +56,3 @@ class SafetyRouter(Safety):
             messages=messages,
             params=params,
         )
-
-    async def run_moderation(self, input: str | list[str], model: str) -> ModerationObject:
-        async def get_shield_id(self, model: str) -> str:
-            """Get Shield id from model (provider_resource_id) of shield."""
-            list_shields_response = await self.routing_table.list_shields()
-
-            matches = [s.identifier for s in list_shields_response.data if model == s.provider_resource_id]
-
-            if not matches:
-                raise ValueError(f"No shield associated with provider_resource id {model}")
-            if len(matches) > 1:
-                raise ValueError(f"Multiple shields associated with provider_resource id {model}")
-            return matches[0]
-
-        shield_id = await get_shield_id(self, model)
-        logger.debug(f"SafetyRouter.run_moderation: {shield_id}")
-        provider = await self.routing_table.get_provider_impl(shield_id)
-
-        response = await provider.run_moderation(
-            input=input,
-            model=model,
-        )
-
-        return response

llama_stack/core/routing_tables/common.py

@@ -60,8 +60,6 @@ async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None:
         return await p.unregister_vector_db(obj.identifier)
     elif api == Api.inference:
         return await p.unregister_model(obj.identifier)
-    elif api == Api.safety:
-        return await p.unregister_shield(obj.identifier)
     elif api == Api.datasetio:
         return await p.unregister_dataset(obj.identifier)
     elif api == Api.tool_runtime:

llama_stack/core/routing_tables/models.py

@@ -63,8 +63,6 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
 
     async def get_provider_impl(self, model_id: str) -> Any:
         model = await lookup_model(self, model_id)
-        if model.provider_id not in self.impls_by_provider_id:
-            raise ValueError(f"Provider {model.provider_id} not found in the routing table")
         return self.impls_by_provider_id[model.provider_id]
 
     async def register_model(

llama_stack/core/routing_tables/shields.py

@@ -55,7 +55,3 @@ class ShieldsRoutingTable(CommonRoutingTableImpl, Shields):
         )
         await self.register_object(shield)
         return shield
-
-    async def unregister_shield(self, identifier: str) -> None:
-        existing_shield = await self.get_shield(identifier)
-        await self.unregister_object(existing_shield)

llama_stack/core/routing_tables/toolgroups.py

@@ -124,7 +124,10 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
         return toolgroup
 
     async def unregister_toolgroup(self, toolgroup_id: str) -> None:
-        await self.unregister_object(await self.get_tool_group(toolgroup_id))
+        tool_group = await self.get_tool_group(toolgroup_id)
+        if tool_group is None:
+            raise ToolGroupNotFoundError(toolgroup_id)
+        await self.unregister_object(tool_group)
 
     async def shutdown(self) -> None:
         pass

llama_stack/core/routing_tables/vector_dbs.py

@@ -8,7 +8,7 @@ from typing import Any
 
 from pydantic import TypeAdapter
 
-from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError, VectorStoreNotFoundError
+from llama_stack.apis.common.errors import ModelNotFoundError, VectorStoreNotFoundError
 from llama_stack.apis.models import ModelType
 from llama_stack.apis.resource import ResourceType
 from llama_stack.apis.vector_dbs import ListVectorDBsResponse, VectorDB, VectorDBs
@@ -66,7 +66,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         if model is None:
             raise ModelNotFoundError(embedding_model)
         if model.model_type != ModelType.embedding:
-            raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding)
+            raise ValueError(f"Model {embedding_model} is not an embedding model")
         if "embedding_dimension" not in model.metadata:
             raise ValueError(f"Model {embedding_model} does not have an embedding dimension")
         vector_db_data = {

llama_stack/core/server/server.py

@@ -9,7 +9,7 @@ import asyncio
 import functools
 import inspect
 import json
-import logging  # allow-direct-logging
+import logging
 import os
 import ssl
 import sys
@@ -21,18 +21,16 @@ from importlib.metadata import version as parse_version
 from pathlib import Path
 from typing import Annotated, Any, get_origin
 
-import httpx
 import rich.pretty
 import yaml
 from aiohttp import hdrs
-from fastapi import Body, FastAPI, HTTPException, Request, Response
+from fastapi import Body, FastAPI, HTTPException, Request
 from fastapi import Path as FastapiPath
 from fastapi.exceptions import RequestValidationError
 from fastapi.responses import JSONResponse, StreamingResponse
 from openai import BadRequestError
 from pydantic import BaseModel, ValidationError
 
-from llama_stack.apis.common.errors import ConflictError, ResourceNotFoundError
 from llama_stack.apis.common.responses import PaginatedResponse
 from llama_stack.cli.utils import add_config_distro_args, get_config_from_args
 from llama_stack.core.access_control.access_control import AccessDeniedError
@@ -117,7 +115,7 @@ def translate_exception(exc: Exception) -> HTTPException | RequestValidationErro
 
     if isinstance(exc, RequestValidationError):
         return HTTPException(
-            status_code=httpx.codes.BAD_REQUEST,
+            status_code=400,
             detail={
                 "errors": [
                     {
@@ -129,25 +127,21 @@ def translate_exception(exc: Exception) -> HTTPException | RequestValidationErro
                 ]
             },
         )
-    elif isinstance(exc, ConflictError):
-        return HTTPException(status_code=409, detail=str(exc))
-    elif isinstance(exc, ResourceNotFoundError):
-        return HTTPException(status_code=404, detail=str(exc))
     elif isinstance(exc, ValueError):
-        return HTTPException(status_code=httpx.codes.BAD_REQUEST, detail=f"Invalid value: {str(exc)}")
+        return HTTPException(status_code=400, detail=f"Invalid value: {str(exc)}")
     elif isinstance(exc, BadRequestError):
-        return HTTPException(status_code=httpx.codes.BAD_REQUEST, detail=str(exc))
+        return HTTPException(status_code=400, detail=str(exc))
     elif isinstance(exc, PermissionError | AccessDeniedError):
-        return HTTPException(status_code=httpx.codes.FORBIDDEN, detail=f"Permission denied: {str(exc)}")
+        return HTTPException(status_code=403, detail=f"Permission denied: {str(exc)}")
     elif isinstance(exc, asyncio.TimeoutError | TimeoutError):
-        return HTTPException(status_code=httpx.codes.GATEWAY_TIMEOUT, detail=f"Operation timed out: {str(exc)}")
+        return HTTPException(status_code=504, detail=f"Operation timed out: {str(exc)}")
     elif isinstance(exc, NotImplementedError):
-        return HTTPException(status_code=httpx.codes.NOT_IMPLEMENTED, detail=f"Not implemented: {str(exc)}")
+        return HTTPException(status_code=501, detail=f"Not implemented: {str(exc)}")
     elif isinstance(exc, AuthenticationRequiredError):
-        return HTTPException(status_code=httpx.codes.UNAUTHORIZED, detail=f"Authentication required: {str(exc)}")
+        return HTTPException(status_code=401, detail=f"Authentication required: {str(exc)}")
     else:
         return HTTPException(
-            status_code=httpx.codes.INTERNAL_SERVER_ERROR,
+            status_code=500,
             detail="Internal server error: An unexpected error occurred.",
         )
 
@@ -186,6 +180,7 @@ async def sse_generator(event_gen_coroutine):
         event_gen = await event_gen_coroutine
         async for item in event_gen:
             yield create_sse_event(item)
+            await asyncio.sleep(0.01)
     except asyncio.CancelledError:
         logger.info("Generator cancelled")
         if event_gen:
@@ -241,10 +236,6 @@ def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable:
                     result = await maybe_await(value)
                     if isinstance(result, PaginatedResponse) and result.url is None:
                         result.url = route
-
-                    if method.upper() == "DELETE" and result is None:
-                        return Response(status_code=httpx.codes.NO_CONTENT)
-
                     return result
             except Exception as e:
                 if logger.isEnabledFor(logging.DEBUG):
@@ -361,7 +352,7 @@ class ClientVersionMiddleware:
                             await send(
                                 {
                                     "type": "http.response.start",
-                                    "status": httpx.codes.UPGRADE_REQUIRED,
+                                    "status": 426,
                                     "headers": [[b"content-type", b"application/json"]],
                                 }
                             )

llama_stack/core/utils/exec.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import importlib
+import logging
 import os
 import signal
 import subprocess
@@ -12,9 +12,9 @@ import sys
 
 from termcolor import cprint
 
-from llama_stack.log import get_logger
+log = logging.getLogger(__name__)
 
-log = get_logger(name=__name__, category="core")
+import importlib
 
 
 def formulate_run_args(image_type: str, image_name: str) -> list: